Analytics Engineer Jobs in Colorado
Analytics Engineer jobs in Colorado are concentrated in Denver, Boulder, and Colorado Springs, where companies like Lockheed Martin, Palantir Technologies, and Dish Network maintain large analytics and data engineering teams. The most active specialties in Colorado listings include dbt and modern data stack development, cloud data warehouse engineering on Snowflake and BigQuery, and pipeline architecture for aerospace, telecom, and financial services clients. Colorado sits among the most active analytics engineering markets in the Mountain West, with openings at both the mid-level and senior tiers. See the openings below and apply to the ones that match your experience.
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About REP:
REP designs world-class, innovative gym equipment that is sold around the world, and we are now expanding into apparel, supplements, athletes, and events. REP was established in 2012 by two brothers who shared a deep passion for fitness. From our humble beginnings of selling bumper plates out of a small garage, REP has experienced remarkable growth, now occupying over 300,000+ square feet of office and distribution space in the USA and internationally. With a dedicated team of over 200+ fitness enthusiasts, our vision is to be the #1 Strength Brand in the World. Our commitment to innovation, customer service, and value sets us apart.
We believe in building a culture centered around fitness, so we specialize in outfitting home gyms, commercial gyms, and strength and conditioning facilities. Our dedication to excellence has been recognized multiple times when we were featured in the Inc. 5000 fastest growing private companies in 2018 and 2021 and then voted the best home strength product launch in 2022 by Garage Gym Experiment. Our team works out together, and we regularly invite fitness superstars to join us. We organize fun challenges to test our limits - and the limits of our equipment.
At REP, we continue to grow and bring new opportunities for those who want to be a part of a winning team. We firmly believe that being surrounded by like-minded individuals fuels creative energy and fosters a fun and collaborative work environment. This role is based in our state-of-the-art headquarters with a bright, spacious, shared workspace; scenic views of the Rocky Mountains; and a kitchen always stocked with healthy snacks, protein powder, and coffee. We offer a hybrid work environment with a minimum of three days in the office, but there's flexibility about which days and hours.
Our Core Values:
- Think Like a Customer - We empathize with our customers; understand the 'why' behind every decision we make
- Progress not Perfection - We don't let perfect get in the way of good; we are better today than we were yesterday
- Positive Attitude - Be a glass half-full kind of person and remember to have fun
- Teamwork without Ego - We put the team above ourselves; we only win together
- Take Initiative - We drive momentum; if there is an opportunity to make us better, we want to hear it
About This Role:
We have already decided to put AI in front of our data. This role decides whether we can trust what it says.
REP's analytics direction is MCP-first: our internal agents, ChatDash (internally built, conversational BI platform), and Claude now query our Microsoft Fabric data platform and other sources directly. Agents are only as accurate as the definitions underneath them. In addition to building pipelines to model data from sources into Fabric, the AI-Native Analytics Engineer builds the semantic models and context layers that let both people and AI agents get accurate, grounded answers from REP's enterprise data.
Where a traditional analytics engineer stops at clean models for dashboards, this role extends the work into the agentic control plane: the certified metric definitions, data skills, evaluation suites, and guardrails that AI depends on to query data and act without hallucinating. And you will do it the AI-native way — using AI coding assistants to take new data from bronze to gold, with generated tests and automated reconciliation, compressing work that traditionally took a data engineer, a QA tester, and rounds of business UAT into one seat. What AI cannot do is the judgment: the spec, the review, and the call on whether the answer is correct. That is the job.
Just as important: at REP, the business builds first. Our end users create working versions of the analytics they need on their own — in Claude or directly in ChatDash — often before certified data or clear semantics exist for that domain. You will not be writing requirements documents and building from scratch. The requirements are the thing the business has already built and is already using. Where certified data and semantics exist, your job is to make sure they are readily accessible through Claude, ChatDash, and other development approaches, so people build on trusted ground from the start. Where they do not, your job is to take the working version and figure out how to get its data flowing systemically, with clear semantics and guardrails, without breaking what the business made.
This is a senior individual contributor role with real ownership. You will own REP's metric definitions of record, develop data pipelines according to a medallion architecture, direct our external data engineering partner as the technical owner, and work alongside our AI Solutions Engineer — they build the agents and tools; you build the governed data those agents rely on.
Essential Functions: Include the following. Other duties may be assigned.
Business-Built Solutions (Harden)
- Treat working versions built by business users in Claude and ChatDash as the requirements: reverse-engineer the questions, logic, sources, and implied metric definitions they encode
- Take those working versions and get their data flowing systemically — replacing ad-hoc extracts, uploads, and one-off queries with governed Fabric pipelines and certified semantics
- Preserve what the business built while hardening it: the user's working version keeps working, now on trusted data with clear definitions and guardrails
- Make certified data and semantics accessible wherever people build — Claude, ChatDash, REP MCP, and other development approaches — so new work starts on governed ground
- Partner with the business builders directly, confirming that the hardened version answers the same questions they were answering before, and explaining any differences
Pipelines and Data Products (Build)
- Build Fabric pipelines and gold data products end to end with AI-assisted development: ingestion into bronze, cleansing and conforming in silver, and gold tables and views ready for reporting and agents
- Generate test suites and automated reconciliation against source-system reports, replacing most of the manual UAT burden on the business
- Derive the spec from the working version: the business builder is the named consumer, their use is the decision, and their current outputs become the acceptance criteria — with freshness targets and a business owner confirmed before productionizing
- Keep transformation code version-controlled, tested, and documented well enough that another engineer — or an agent — can extend it without asking
Semantic Layer and Context Engineering (Trust)
- Deliver a certified semantic layer over REP's Fabric gold layer — metrics, entity definitions, join paths, and grain rules — that REP MCP and ChatDash query by default, starting with inventory and net sales
- Establish one definition of record per metric, retiring or explicitly mapping conflicting legacy definitions across Power BI, Polar, and NetSuite
- Structure business rules, metadata, and documentation as durable working memory for agents, not after-the-fact commentary
- Resolve data-trust escalations at the root — in definitions and lineage — rather than through one-off reconciliations
AI Answer Accuracy (Evaluate)
- Stand up an evaluation framework: a question bank with known-correct answers and automated regression checks run on every semantic-layer or model change
- Publish a regular accuracy scorecard so leaders and the AI Governance committee know where agent answers can be trusted — and where they cannot
- Catch regressions before release, not after a business user reports them
Governance Guardrails (Protect)
- Implement access controls and row- and column-level policy in the semantic and MCP layers so agents only return data the requesting user is authorized to see
- Define which agent actions are read-only versus write-capable, and review write paths against REP's connector governance framework before exposure
- Maintain auditable lineage from any agent-delivered answer back to source, sufficient for Finance and governance review
Vendor Direction (Own)
- Act as technical owner of our external data engineering partner: write build specs precise enough to execute and measure against
- Review delivered work against acceptance criteria and reject what misses the bar — professionally and with evidence
- Keep vendor scopes bounded to vertical, testable work so REP is never dependent on a vendor to understand its own data
What You Will Bring:
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- A hardener, not a gatekeeper: You see a business user's working prototype as a gift, not a mess. You can read what they built, understand the intent, and harden it without taking ownership away from them.
- A semantic and context engineer: You know when a problem belongs in the semantic layer versus a prompt, application logic, or an ad-hoc query — and you can defend the line. You can tell a definitional problem from a grain problem from a source-system problem.
- Verification-minded: You assume AI-generated output is wrong until tested, and you build the test before trusting the result. You say so plainly when confidence is low.
- AI-native, not AI-dependent: You treat AI coding assistants as a velocity multiplier under review, not an authority. You read and test what they produce before it ships.
- A governance owner: You apply governance to your own work without being asked, make the conservative call on data exposure when scope is ambiguous, and hold the architecture gates under pressure.
- A technical owner of partners: You translate business needs into specs a vendor can execute, and you are comfortable telling a partner their work does not meet the bar.
Education and/or Experience:
- Bachelor's degree (B. A.) from four-year college or university
- 5+ years in analytics engineering, data engineering, or a closely related role, operating as a senior individual contributor
- Written high-performance SQL and Python dataflows against a cloud warehouse — Microsoft Fabric preferred; Snowflake, Databricks, or BigQuery experience transfers
- Built and maintained models in a medallion (bronze/silver/gold) architecture with dbt-style transformation, testing, and documentation discipline
- Designed or owned a semantic layer or metrics layer that served as the single source of truth for business metrics
- Used AI coding assistants (e.g., Claude Code, Cursor, Copilot) to materially accelerate data pipeline delivery
- Built or contributed to evaluation frameworks for LLM or text-to-SQL accuracy, or exposed governed data to AI agents via MCP or similar interfaces (a strong plus)
- Worked across messy source systems such as NetSuite, Shopify, Amazon SP-API, or HubSpot without having the model handed to you
- Implemented row- and column-level security, access policy, and lineage in a production data platform
- Taken analyst- or business-built prototypes (spreadsheets, notebooks, BI workbooks, or AI-generated reports) and turned them into production data products
- Directed external data engineering partners through specs and acceptance testing
Core Competencies:
Action Oriented - Taking on new opportunities and tough challenges with a sense of urgency, high energy, and enthusiasm.
Resourcefulness - Securing and deploying resources effectively and efficiently.
Collaborates - Building partnerships and working collaboratively with others to meet shared objectives.
Self-Development - Actively seeking new ways to grow and be challenged using both formal and informal development channels.
Semantic & Context Engineering – Designing metric definitions, join paths, and context that let agents query data deterministically and accurately.
Technical Craftsmanship – Writing tested, documented, production-grade SQL and Python across the modern data stack.
Verification & Intellectual Honesty – Treating accuracy as an ongoing, measured system and communicating its limits before they surprise anyone.
Governance & Risk Ownership – Making agent data access safe by default and holding the line without supervision.
Productionizing Business-Built Work – Turning working prototypes into governed, systemic data products while keeping the builder in the loop.
Partner Direction – Specifying, reviewing, and accepting or rejecting vendor work as REP's technical owner.
What's in it for You:
Our commitment to quality and transparency applies not only to our products, but to our people. We are focused on creating a fun, exciting, collaborative space that's centered around health and well-being. We empower everyone on our team to take control of their careers and balance their work and life in a modern, fast-paced environment.
We offer an exceptional compensation package which includes:
Compensation base range: $120,0000 - $140,000 annually with bonus potential
Day One Benefits!
- Medical, Dental, Vision (Competitive Benefits Packages Available)
- Health Savings Account (HSA) with employer contributions
- Flexible Spending Account (FSA) options
- LTD/STD, Life and AD&D (100% premiums covered by REP)
- 401k and Roth options with employer match up to 4%
- Employee Assistance Program (EAP)
Recharge Yourself!
- 4 weeks of PTO accrued during your first year
- 48 hours of sick leave annually
- 7 paid company holidays with an additional floating holiday
- Paid Parental leave day 1
- Birthday - Paid day off
Pumped-up Perks!
- Unique bonus structure that rewards exceptional individual performance as well as company-wide performance
- Full access to employee gym
- Employee discount on REP equipment, apparel and nutrition
- Daily snacks and drinks provided by REP (for onsite employees)
- 'Fitness' casual attire
- Company and team building events
- Professional growth and development within the organization
- LIFE-WORK balance
Physical Requirements:
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this job, the employee is constantly required to sit and use hands to operate the telephone, type and operate the computer and mouse. Physical effort may include stooping, kneeling, touching, feeling, reaching, standing, walking, pushing, pulling, lifting, fingering, grasping, talking, hearing, and repetitive motions. The employee is frequently required to talk, hear, and bend and twist their necks. The employee may occasionally lift and/or move up to 00 pounds and rarely lift and/or move up to 00 pounds assisted. Specific vision abilities required by this job include close vision and distance vision.
REP provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, or genetics. In addition to federal law requirements, REP complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities.
This specification is intended to indicate the kinds of tasks and levels of work difficulty that will be required of positions that will be given this title and shall not be construed as declaring what the specific duties and responsibilities of any particular position shall be. It is not intended to limit or in any way modify the right of any supervisor to assign, direct, and control the work of employees under his/her supervision. The use of a particular expression or illustration describing duties shall not be held to exclude other duties not mentioned. Ability to competently perform all the essential duties of the position, with or without reasonable accommodation, demonstrated commitment to effective customer service delivery, and the ability to work productively as a member of a team or work group are basic requirements of all positions at REP.
About REP:
REP designs world-class, innovative gym equipment that is sold around the world, and we are now expanding into apparel, supplements, athletes, and events. REP was established in 2012 by two brothers who shared a deep passion for fitness. From our humble beginnings of selling bumper plates out of a small garage, REP has experienced remarkable growth, now occupying over 300,000+ square feet of office and distribution space in the USA and internationally. With a dedicated team of over 200+ fitness enthusiasts, our vision is to be the #1 Strength Brand in the World. Our commitment to innovation, customer service, and value sets us apart.
We believe in building a culture centered around fitness, so we specialize in outfitting home gyms, commercial gyms, and strength and conditioning facilities. Our dedication to excellence has been recognized multiple times when we were featured in the Inc. 5000 fastest growing private companies in 2018 and 2021 and then voted the best home strength product launch in 2022 by Garage Gym Experiment. Our team works out together, and we regularly invite fitness superstars to join us. We organize fun challenges to test our limits - and the limits of our equipment.
At REP, we continue to grow and bring new opportunities for those who want to be a part of a winning team. We firmly believe that being surrounded by like-minded individuals fuels creative energy and fosters a fun and collaborative work environment. This role is based in our state-of-the-art headquarters with a bright, spacious, shared workspace; scenic views of the Rocky Mountains; and a kitchen always stocked with healthy snacks, protein powder, and coffee. We offer a hybrid work environment with a minimum of three days in the office, but there's flexibility about which days and hours.
Our Core Values:
- Think Like a Customer - We empathize with our customers; understand the 'why' behind every decision we make
- Progress not Perfection - We don't let perfect get in the way of good; we are better today than we were yesterday
- Positive Attitude - Be a glass half-full kind of person and remember to have fun
- Teamwork without Ego - We put the team above ourselves; we only win together
- Take Initiative - We drive momentum; if there is an opportunity to make us better, we want to hear it
About This Role:
We have already decided to put AI in front of our data. This role decides whether we can trust what it says.
REP's analytics direction is MCP-first: our internal agents, ChatDash (internally built, conversational BI platform), and Claude now query our Microsoft Fabric data platform and other sources directly. Agents are only as accurate as the definitions underneath them. In addition to building pipelines to model data from sources into Fabric, the AI-Native Analytics Engineer builds the semantic models and context layers that let both people and AI agents get accurate, grounded answers from REP's enterprise data.
Where a traditional analytics engineer stops at clean models for dashboards, this role extends the work into the agentic control plane: the certified metric definitions, data skills, evaluation suites, and guardrails that AI depends on to query data and act without hallucinating. And you will do it the AI-native way — using AI coding assistants to take new data from bronze to gold, with generated tests and automated reconciliation, compressing work that traditionally took a data engineer, a QA tester, and rounds of business UAT into one seat. What AI cannot do is the judgment: the spec, the review, and the call on whether the answer is correct. That is the job.
Just as important: at REP, the business builds first. Our end users create working versions of the analytics they need on their own — in Claude or directly in ChatDash — often before certified data or clear semantics exist for that domain. You will not be writing requirements documents and building from scratch. The requirements are the thing the business has already built and is already using. Where certified data and semantics exist, your job is to make sure they are readily accessible through Claude, ChatDash, and other development approaches, so people build on trusted ground from the start. Where they do not, your job is to take the working version and figure out how to get its data flowing systemically, with clear semantics and guardrails, without breaking what the business made.
This is a senior individual contributor role with real ownership. You will own REP's metric definitions of record, develop data pipelines according to a medallion architecture, direct our external data engineering partner as the technical owner, and work alongside our AI Solutions Engineer — they build the agents and tools; you build the governed data those agents rely on.
Essential Functions: Include the following. Other duties may be assigned.
Business-Built Solutions (Harden)
- Treat working versions built by business users in Claude and ChatDash as the requirements: reverse-engineer the questions, logic, sources, and implied metric definitions they encode
- Take those working versions and get their data flowing systemically — replacing ad-hoc extracts, uploads, and one-off queries with governed Fabric pipelines and certified semantics
- Preserve what the business built while hardening it: the user's working version keeps working, now on trusted data with clear definitions and guardrails
- Make certified data and semantics accessible wherever people build — Claude, ChatDash, REP MCP, and other development approaches — so new work starts on governed ground
- Partner with the business builders directly, confirming that the hardened version answers the same questions they were answering before, and explaining any differences
Pipelines and Data Products (Build)
- Build Fabric pipelines and gold data products end to end with AI-assisted development: ingestion into bronze, cleansing and conforming in silver, and gold tables and views ready for reporting and agents
- Generate test suites and automated reconciliation against source-system reports, replacing most of the manual UAT burden on the business
- Derive the spec from the working version: the business builder is the named consumer, their use is the decision, and their current outputs become the acceptance criteria — with freshness targets and a business owner confirmed before productionizing
- Keep transformation code version-controlled, tested, and documented well enough that another engineer — or an agent — can extend it without asking
Semantic Layer and Context Engineering (Trust)
- Deliver a certified semantic layer over REP's Fabric gold layer — metrics, entity definitions, join paths, and grain rules — that REP MCP and ChatDash query by default, starting with inventory and net sales
- Establish one definition of record per metric, retiring or explicitly mapping conflicting legacy definitions across Power BI, Polar, and NetSuite
- Structure business rules, metadata, and documentation as durable working memory for agents, not after-the-fact commentary
- Resolve data-trust escalations at the root — in definitions and lineage — rather than through one-off reconciliations
AI Answer Accuracy (Evaluate)
- Stand up an evaluation framework: a question bank with known-correct answers and automated regression checks run on every semantic-layer or model change
- Publish a regular accuracy scorecard so leaders and the AI Governance committee know where agent answers can be trusted — and where they cannot
- Catch regressions before release, not after a business user reports them
Governance Guardrails (Protect)
- Implement access controls and row- and column-level policy in the semantic and MCP layers so agents only return data the requesting user is authorized to see
- Define which agent actions are read-only versus write-capable, and review write paths against REP's connector governance framework before exposure
- Maintain auditable lineage from any agent-delivered answer back to source, sufficient for Finance and governance review
Vendor Direction (Own)
- Act as technical owner of our external data engineering partner: write build specs precise enough to execute and measure against
- Review delivered work against acceptance criteria and reject what misses the bar — professionally and with evidence
- Keep vendor scopes bounded to vertical, testable work so REP is never dependent on a vendor to understand its own data
What You Will Bring:
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- A hardener, not a gatekeeper: You see a business user's working prototype as a gift, not a mess. You can read what they built, understand the intent, and harden it without taking ownership away from them.
- A semantic and context engineer: You know when a problem belongs in the semantic layer versus a prompt, application logic, or an ad-hoc query — and you can defend the line. You can tell a definitional problem from a grain problem from a source-system problem.
- Verification-minded: You assume AI-generated output is wrong until tested, and you build the test before trusting the result. You say so plainly when confidence is low.
- AI-native, not AI-dependent: You treat AI coding assistants as a velocity multiplier under review, not an authority. You read and test what they produce before it ships.
- A governance owner: You apply governance to your own work without being asked, make the conservative call on data exposure when scope is ambiguous, and hold the architecture gates under pressure.
- A technical owner of partners: You translate business needs into specs a vendor can execute, and you are comfortable telling a partner their work does not meet the bar.
Education and/or Experience:
- Bachelor's degree (B. A.) from four-year college or university
- 5+ years in analytics engineering, data engineering, or a closely related role, operating as a senior individual contributor
- Written high-performance SQL and Python dataflows against a cloud warehouse — Microsoft Fabric preferred; Snowflake, Databricks, or BigQuery experience transfers
- Built and maintained models in a medallion (bronze/silver/gold) architecture with dbt-style transformation, testing, and documentation discipline
- Designed or owned a semantic layer or metrics layer that served as the single source of truth for business metrics
- Used AI coding assistants (e.g., Claude Code, Cursor, Copilot) to materially accelerate data pipeline delivery
- Built or contributed to evaluation frameworks for LLM or text-to-SQL accuracy, or exposed governed data to AI agents via MCP or similar interfaces (a strong plus)
- Worked across messy source systems such as NetSuite, Shopify, Amazon SP-API, or HubSpot without having the model handed to you
- Implemented row- and column-level security, access policy, and lineage in a production data platform
- Taken analyst- or business-built prototypes (spreadsheets, notebooks, BI workbooks, or AI-generated reports) and turned them into production data products
- Directed external data engineering partners through specs and acceptance testing
Core Competencies:
Action Oriented - Taking on new opportunities and tough challenges with a sense of urgency, high energy, and enthusiasm.
Resourcefulness - Securing and deploying resources effectively and efficiently.
Collaborates - Building partnerships and working collaboratively with others to meet shared objectives.
Self-Development - Actively seeking new ways to grow and be challenged using both formal and informal development channels.
Semantic & Context Engineering – Designing metric definitions, join paths, and context that let agents query data deterministically and accurately.
Technical Craftsmanship – Writing tested, documented, production-grade SQL and Python across the modern data stack.
Verification & Intellectual Honesty – Treating accuracy as an ongoing, measured system and communicating its limits before they surprise anyone.
Governance & Risk Ownership – Making agent data access safe by default and holding the line without supervision.
Productionizing Business-Built Work – Turning working prototypes into governed, systemic data products while keeping the builder in the loop.
Partner Direction – Specifying, reviewing, and accepting or rejecting vendor work as REP's technical owner.
What's in it for You:
Our commitment to quality and transparency applies not only to our products, but to our people. We are focused on creating a fun, exciting, collaborative space that's centered around health and well-being. We empower everyone on our team to take control of their careers and balance their work and life in a modern, fast-paced environment.
We offer an exceptional compensation package which includes:
Compensation base range: $120,0000 - $140,000 annually with bonus potential
Day One Benefits!
- Medical, Dental, Vision (Competitive Benefits Packages Available)
- Health Savings Account (HSA) with employer contributions
- Flexible Spending Account (FSA) options
- LTD/STD, Life and AD&D (100% premiums covered by REP)
- 401k and Roth options with employer match up to 4%
- Employee Assistance Program (EAP)
Recharge Yourself!
- 4 weeks of PTO accrued during your first year
- 48 hours of sick leave annually
- 7 paid company holidays with an additional floating holiday
- Paid Parental leave day 1
- Birthday - Paid day off
Pumped-up Perks!
- Unique bonus structure that rewards exceptional individual performance as well as company-wide performance
- Full access to employee gym
- Employee discount on REP equipment, apparel and nutrition
- Daily snacks and drinks provided by REP (for onsite employees)
- 'Fitness' casual attire
- Company and team building events
- Professional growth and development within the organization
- LIFE-WORK balance
Physical Requirements:
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this job, the employee is constantly required to sit and use hands to operate the telephone, type and operate the computer and mouse. Physical effort may include stooping, kneeling, touching, feeling, reaching, standing, walking, pushing, pulling, lifting, fingering, grasping, talking, hearing, and repetitive motions. The employee is frequently required to talk, hear, and bend and twist their necks. The employee may occasionally lift and/or move up to 00 pounds and rarely lift and/or move up to 00 pounds assisted. Specific vision abilities required by this job include close vision and distance vision.
REP provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, or genetics. In addition to federal law requirements, REP complies with applicable state and local laws governing nondiscrimination in employment in every location in which the company has facilities.
This specification is intended to indicate the kinds of tasks and levels of work difficulty that will be required of positions that will be given this title and shall not be construed as declaring what the specific duties and responsibilities of any particular position shall be. It is not intended to limit or in any way modify the right of any supervisor to assign, direct, and control the work of employees under his/her supervision. The use of a particular expression or illustration describing duties shall not be held to exclude other duties not mentioned. Ability to competently perform all the essential duties of the position, with or without reasonable accommodation, demonstrated commitment to effective customer service delivery, and the ability to work productively as a member of a team or work group are basic requirements of all positions at REP.
See All 8 Analytics Engineer Jobs in Colorado
Find roles in Colorado that match your experience and apply in just a few clicks.
Find Analytics Engineer JobsAnalytics Engineer Jobs by City in Colorado
Where Colorado roles are concentrated, by current openings.
Analytics Engineer Job Market in Colorado
A snapshot from current Colorado openings, updated as new roles post.
Who's Hiring
What Colorado Employers Look For
The qualifications that appear most often in analytics engineer jobs across Colorado.
- Bachelor's degree in computer science, statistics, engineering, or a related analytical field
- Proficiency in SQL and at least one programming language such as Python or Scala
- Hands-on experience with dbt, Airflow, or a comparable data transformation and orchestration tool
- Experience building and maintaining pipelines on Snowflake, BigQuery, or Redshift in a cloud environment
- Ability to collaborate with data scientists, analysts, and software engineers on cross-functional teams
- Familiarity with version control using Git and experience working within agile development workflows
Analytics Engineer Jobs in Colorado: Frequently Asked Questions
How do you become a analytics engineer in Colorado?
Analytics engineering in Colorado has no state-issued license or registration requirement. Most hiring managers expect a bachelor's degree in computer science, statistics, mathematics, or a closely related field, combined with demonstrated experience in SQL, data modeling, and at least one modern data stack tool such as dbt. Colorado employers place strong weight on a portfolio of production-quality data pipelines, and industry certifications from Snowflake, dbt Labs, or Google Cloud are recognized as meaningful credentials here.
How much do analytics engineers make in Colorado?
Analytics engineers in Colorado earn a median of about $117,400 a year, based on May 2025 Bureau of Labor Statistics wage data, ranging from around $74,470 for the lowest 10% to over $189,630 for the top 10%. Pay rises with experience, specialty, and employer.
Which companies hire analytics engineers in Colorado?
Colorado analytics engineer roles are posted by Amazon Web Services, Rep Fitness, and Digible and others right now, based on current listings on Migrate Mate as of November 2026. Colorado's mix of aerospace contractors, telecom firms, and a growing financial technology sector means demand comes from a wide range of industries, not just software companies.
Which Colorado cities have the most analytics engineer jobs?
Denver, Englewood, and Westminster have the most analytics engineer openings in Colorado. Denver drives the largest share given its concentration of corporate headquarters, financial services firms, and tech employers, while Boulder attracts data-focused roles tied to its research institutions and startup ecosystem, and Colorado Springs benefits from the significant defense and aerospace presence centered around Peterson Space Force Base and nearby contractors.
Are there remote analytics engineer jobs in Colorado?
Yes, and more than most fields. About 100% of analytics engineer openings tied to Colorado are remote or hybrid as of November 2026, reflecting how naturally the work fits distributed collaboration. The pipeline development and data modeling portions of the role are especially well suited to remote arrangements, while stakeholder-facing work and cross-team sprint planning tend to favor hybrid schedules at larger Colorado employers.
How can I get hired as a analytics engineer in Colorado with little or no experience?
The most realistic entry path is moving from a business intelligence analyst or data analyst role, where SQL fluency and dashboard work translate directly into analytics engineering responsibilities. Colorado's large employers in aerospace and financial services, including defense contractors along the Front Range, regularly bring on associate data engineers and offer rotational data programs for recent graduates. Building a public portfolio using dbt and a cloud warehouse on a Colorado open dataset, such as those published by the Colorado Information Marketplace, gives a candidate a concrete, local credential that stands out to hiring managers here.
Where can I find and apply to analytics engineer jobs in Colorado?
You can find and apply to analytics engineer jobs in Colorado on Migrate Mate, which lists current Colorado openings. Search the listings to find roles that fit your experience level, specialization, and preferred location or remote arrangement, then apply directly to the ones that match.
See All 8 Analytics Engineer Jobs in Colorado
Find roles in Colorado that match your experience and apply in just a few clicks.
Find Analytics Engineer Jobs