Data Analytics Engineer Jobs
Data Analytics Engineer jobs are open across tech, finance, healthcare, and retail, from junior to staff and principal level, with specializations in data modeling, pipeline architecture, and analytics infrastructure. Find a role that fits from the openings below and apply directly.
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Showing 5 of 1,349+ Data Analytics Engineer jobs
- Advises the Product Solutions teams on solutioning and adopting new and existing client-facing products and capabilities while crafting complex solutions and assessing risk to enhance the customer experience
- Leverages extensive knowledge of a cluster of products and capabilities to manage the strategic development of end-to-end product solution strategies and processes
- Partners with Sales to advise on strategic pricing for deals, contributes to the development of sales training and collateral, and oversees Request for Proposal (RFP) responses
- Manages the collection of client feedback and oversees the delivery of feedback to Product teams
- Drives the development of strategy and roadmaps for data governance initiatives, including next-generation data governance tooling and AI-for-data capabilities
- Collaborates with stakeholders, subject matter experts, and engineers to understand use cases, requirements, and dependencies, critically assessing proposed solutions.
- Communicates complex ideas effectively to collaborators and senior leaders using precise terminology and relatable examples
- Balances timeliness with quality under tight deadlines, managing multiple priorities and cross-functional partnerships.
- Ensures end-to-end relevance to stakeholder needs, from gathering business requirements and working with technology teams to achieve successful delivery
- Defines and refines customer-centric solution approaches that connect data governance capabilities to measurable business outcomes.
- Partners with business, technology, and control stakeholders to deploy solutions into production effectively and responsibly
- 8+ years of experience or equivalent expertise leading and developing solutions across multiple teams and a cluster of products
- Extensive experience facilitating sales cycle activities and developing and optimizing strategies and processes
- Demonstrable experience structuring and handling complex solutions for business problems to meet clients' needs
- Understanding and hands-on experience building agentic AI systems within regulated or compliance-driven environments
- 8+ years of experience developing enterprise-wide data, data governance, or AI strategy for large, complex organizations.
- 8+ years of experience either as a product manager, product designer, engineer, data analyst, data scientist, or user researcher
- Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences
- Curious, hardworking, and detail-oriented, and motivated by complex analytical problems.
- Ability to collaborate effectively with stakeholders, subject matter experts, and engineers to translate needs into clear requirements
- Ability to balance quality and speed under tight deadlines while managing multiple priorities and partnerships.
- Ability to drive end-to-end delivery—from business requirements through implementation—ensuring outcomes meet stakeholder needs
- Direct experience with MCP (Model Context Protocol) designing tool schemas, building MCP servers, managing tool surface exposure, or integrating MCP into an agent platform
- Experience in regulated industries (financial services, healthcare, or government) with practical exposure to model risk management, audit trails, and compliance-driven engineering constraints.
- Familiarity with agent security concerns: prompt injection, tool misuse, over-privileged tool access, and blast radius containment strategies
- Experience building evaluation frameworks for LLM-based systems, including production-grade evaluation pipelines with structured outputs and regression tracking.
- Exposure to cloud-native AI infrastructure (managed model endpoints, model gateways, token/cost observability, and multi-tenant serving considerations)
- Experience contributing to developer-facing SDK or platform tooling (designing APIs, writing effective documentation, iterating based on adoption signals).
- Familiarity with responsible AI practices as they apply to agents, including human oversight requirements, escalation paths, intervention hooks, and auditability standards
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
ABOUT THE TEAM
Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.
- Advises the Product Solutions teams on solutioning and adopting new and existing client-facing products and capabilities while crafting complex solutions and assessing risk to enhance the customer experience
- Leverages extensive knowledge of a cluster of products and capabilities to manage the strategic development of end-to-end product solution strategies and processes
- Partners with Sales to advise on strategic pricing for deals, contributes to the development of sales training and collateral, and oversees Request for Proposal (RFP) responses
- Manages the collection of client feedback and oversees the delivery of feedback to Product teams
- Drives the development of strategy and roadmaps for data governance initiatives, including next-generation data governance tooling and AI-for-data capabilities
- Collaborates with stakeholders, subject matter experts, and engineers to understand use cases, requirements, and dependencies, critically assessing proposed solutions.
- Communicates complex ideas effectively to collaborators and senior leaders using precise terminology and relatable examples
- Balances timeliness with quality under tight deadlines, managing multiple priorities and cross-functional partnerships.
- Ensures end-to-end relevance to stakeholder needs, from gathering business requirements and working with technology teams to achieve successful delivery
- Defines and refines customer-centric solution approaches that connect data governance capabilities to measurable business outcomes.
- Partners with business, technology, and control stakeholders to deploy solutions into production effectively and responsibly
- 8+ years of experience or equivalent expertise leading and developing solutions across multiple teams and a cluster of products
- Extensive experience facilitating sales cycle activities and developing and optimizing strategies and processes
- Demonstrable experience structuring and handling complex solutions for business problems to meet clients' needs
- Understanding and hands-on experience building agentic AI systems within regulated or compliance-driven environments
- 8+ years of experience developing enterprise-wide data, data governance, or AI strategy for large, complex organizations.
- 8+ years of experience either as a product manager, product designer, engineer, data analyst, data scientist, or user researcher
- Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences
- Curious, hardworking, and detail-oriented, and motivated by complex analytical problems.
- Ability to collaborate effectively with stakeholders, subject matter experts, and engineers to translate needs into clear requirements
- Ability to balance quality and speed under tight deadlines while managing multiple priorities and partnerships.
- Ability to drive end-to-end delivery—from business requirements through implementation—ensuring outcomes meet stakeholder needs
- Direct experience with MCP (Model Context Protocol) designing tool schemas, building MCP servers, managing tool surface exposure, or integrating MCP into an agent platform
- Experience in regulated industries (financial services, healthcare, or government) with practical exposure to model risk management, audit trails, and compliance-driven engineering constraints.
- Familiarity with agent security concerns: prompt injection, tool misuse, over-privileged tool access, and blast radius containment strategies
- Experience building evaluation frameworks for LLM-based systems, including production-grade evaluation pipelines with structured outputs and regression tracking.
- Exposure to cloud-native AI infrastructure (managed model endpoints, model gateways, token/cost observability, and multi-tenant serving considerations)
- Experience contributing to developer-facing SDK or platform tooling (designing APIs, writing effective documentation, iterating based on adoption signals).
- Familiarity with responsible AI practices as they apply to agents, including human oversight requirements, escalation paths, intervention hooks, and auditability standards
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.
JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans
ABOUT THE TEAM
Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we're setting our businesses, clients, customers and employees up for success.
Data Analytics Engineer Jobs by Experience Level
Top Cities Hiring Data Analytics Engineers
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Find Data Analytics Engineer JobsData Analytics Engineer Job Market
Who's Hiring
- CoStar26
- Capital One24
- JPMorganChase23
- American Express22
- PNC Financial Services20
Top Industries Hiring
- Technology & Software19
- Insurance5
- Energy4
- Consulting & Professional Services4
- Retail3
What Employers Look For
The qualifications that appear most often in data analytics engineer jobs.
- Proficiency in SQL and at least one analytical language such as Python
- Experience with cloud data warehouses like Snowflake, BigQuery, or Redshift
- Hands-on experience building and maintaining dbt models in production
- Familiarity with orchestration tools such as Airflow, Prefect, or Dagster
- Bachelor's degree in computer science, statistics, engineering, or a related field
- Ability to translate business questions into dimensional data models and schemas
Tips for Your Data Analytics Engineer Job Search
Tailor your resume to the stack
Job listings for data analytics engineers almost always name a specific stack: dbt, Snowflake, BigQuery, Spark. Mirror the exact tool names from each posting in your resume's skills and experience sections so your application clears automated screening.
Show work in your portfolio
Hiring teams want to see how you build. Link a GitHub repo with a real dbt project or a documented pipeline you've designed. Describing what you built in bullet points alone won't set you apart the way working code does.
Target roles by data maturity stage
A startup's data analytics engineer role often means building the warehouse from scratch, while a large enterprise role means maintaining and optimizing one that already exists. Read the job description for clues about which stage you're walking into before you apply.
Apply early to roles that fit
Migrate Mate lists data analytics engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prep for a technical take-home
Most data analytics engineer interviews include a take-home that tests SQL transformation logic, data modeling decisions, or pipeline design. Practice writing clean, documented dbt models and be ready to walk through your architectural choices out loud.
Negotiate scope not just salary
When you reach the offer stage, ask about the size of the data team, ownership of the roadmap, and how decisions about tooling get made. These factors shape whether you'll grow in the role far more than the initial title will.
Data Analytics Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most data analytics engineers?
The companies hiring the most data analytics engineers right now include CoStar, Capital One, and JPMorganChase, with the largest share of openings in California, New York, and Virginia, based on current listings on Migrate Mate as of September 2026. Demand is particularly concentrated in companies undergoing cloud data warehouse migrations or scaling self-serve analytics programs.
How many data analytics engineer jobs are remote?
About 73% of data analytics engineer openings are fully remote or hybrid as of September 2026, making it one of the more location-flexible roles in the data field. Fully remote options are most common in pipeline engineering and data modeling work, where collaboration happens asynchronously through code review and documentation rather than in-person sessions.
How do you become a data analytics engineer?
Start by building strong SQL skills and learning a transformation framework like dbt, since those appear in nearly every job posting. Then get hands-on experience with a cloud warehouse by building a personal project using a free tier. Add Python for scripting and automation, learn the basics of a pipeline orchestration tool, and document everything in a public GitHub repo that hiring managers can review.
Can you get a data analytics engineer job with little experience?
Yes, but you'll need to close the experience gap with demonstrable project work. Build end-to-end pipelines in a personal or open-source project using the tools employers actually hire for, such as dbt and Snowflake. Roles titled junior analytics engineer or analytics engineer at smaller companies are your most realistic entry point, since they often expect candidates to grow into the full stack on the job.
What does the data analytics engineer interview process look like?
Most hiring processes start with a recruiter screen, move to a technical phone interview focused on SQL and data modeling concepts, and then include a take-home or live coding exercise where you build or critique a dbt model or pipeline design. A final round typically involves a system design discussion where you walk through how you'd architect an analytics solution, followed by a culture or cross-functional fit conversation with stakeholders.
Where can I find and apply to data analytics engineer jobs?
You can find and apply to data analytics engineer jobs on Migrate Mate, which lists current openings from across the United States. Find roles that match your experience and specialization, then apply directly to each listing. No detours or redirects, just a straightforward application to the roles that fit.
See All 1,349+ Data Analytics Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
Find Data Analytics Engineer Jobs