Remote Staff Analytics Engineer Jobs
Remote staff analytics engineer jobs are actively hiring across the U.S. at remote-first firms and distributed data teams in technology, finance, healthcare, and media. Companies currently posting remote openings include ServiceNow, Openly, and Fanatics. Find a role that fits below and apply directly.
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Showing 5 of 10+ Remote Staff Analytics Engineer jobs
About Us
Overview
You own the data foundation behind how CX Operations understands its performance and how it connects to the broader Fanatics ecosystem. As the Staff Analyst for Ops Ecosystem Analytics, you design and build the architecture, models, and operating standards that make CX Ops ecosystem data in Snowflake trustworthy, scalable, and usable by cross-functional teams, leaders, and by the AI agents we're building on top of it.
This is a hands-on technical role with broad influence. You take raw tables from across the business and turn them into clean, documented, tested models that the rest of the team builds on. You work side by side with Data & Analytics Engineering as well as Strategy teams. You set the SQL and data-modeling bar for the Ops Analytics team without managing anyone. You tell the story of what the data says, regularly presenting to senior leadership and translating architecture decisions and analytical findings into impact they can act on.
We are building toward an AI-first way of operating, and this role is central to it. The right person is technically sharp, relentlessly detail-oriented, and curious about both what the data is saying and how AI changes the way we find and act on answers. You move fast, own ambiguous problems, and hold yourself to a high bar without being told to.
What You'll Do
Data architecture and operations environment
- Own the architecture of CX Ops ecosystem data in Snowflake, including schema design, data layers, access patterns, and the operating model that keeps it reliable as it scales.
- Decide how ecosystem Ops data should be structured, owned, and governed, and drive alignment with necessary teams, like Data Engineering, on those decisions.
- Consolidate overlapping or one-off datasets into shared, well-governed sources of truth, and set naming and documentation conventions.
- Define and track data quality standards for Ops ecosystem data.
- Build alerting on critical metrics as needed so issues are caught before they reach leadership reporting.
dbt and analytics engineering
- Turn raw cross-domain tables into clean, tested, reusable dbt models that the whole team can build on.
- Own the team's core models and introduce data tests, code review practices, and modeling standards that catch issues before they reach stakeholders.
- Review teammates' SQL, models, and dashboards for correctness, performance, and readability, and coach analysts on best practices.
- Replace manual, one-off data pulls with automated, always-on solutions.
Analytical work and insights
- Lead complex, cross-functional analyses from an ambiguous business problem through scoping, analysis, and a decision-ready recommendation, without formal authority over contributors.
- Diagnose root causes rather than symptoms, and surface trends, anomalies, and risks without waiting to be asked.
- Support scenario analysis and impact sizing for product initiatives, operational changes, and staffing decisions.
- Set the standard for publication-ready dashboards and reporting, with clearly defined KPIs and documented assumptions.
Communication and influence
- Present findings, architecture decisions, and impact to Ops and senior leadership. Take a high-uncertainty situation and communicate what you know, what you don't, and what you recommend.
- Build the narrative around the data so stakeholders understand not just what the numbers say but what to do about them.
- Mentor and support analysts across the team through standards, reviews, and knowledge sharing.
AI enablement
- Make sure agents are built on validated, trusted data foundations, and contribute the data models and evaluation rigor they need before they move to build.
- Track the performance of deployed agents against Ops goals.
- Bring a point of view on where AI can be trusted in analytical workflows and where human judgment needs to stay in the loop.
Required Qualifications
- 5+ years in analytics, analytics engineering, business intelligence, or strategic operations.
- Advanced, hands-on SQL. You write, QA, and optimize complex queries and set the standard others are held to. This role includes a live, on-screen technical exercise.
- Strong dbt experience. You have designed model layers, written tests, and made sound architectural decisions, not just maintained existing models.
- Demonstrated experience designing or owning a data environment in a cloud data warehouse (Snowflake preferred), including schema design, governance, and data quality.
- Experience working closely with data or analytics engineering teams to turn raw data into production-ready models.
- Strong executive communication. You have presented analysis and recommendations to senior leaders and can defend your methodology under scrutiny.
- Experience owning complex, cross-functional projects end to end.
- Experience building dashboards in Sigma, Tableau, or a comparable tool.
- High attention to detail. You catch data quality issues before they surface in leadership reporting.
- Comfortable in fast-paced, ambiguous environments with shifting priorities.
Preferred Qualifications
- Experience supporting AI/ML workflows, agent builds, or automation initiatives.
- Python or a similar scripting language for data manipulation and automation.
- Familiarity with operational KPIs across customer support, fraud, payments, or workforce management.
- Experience in gaming, fintech, or sports, or another operationally intensive, high-volume environment.
- Bachelor's degree in Analytics, Computer Science, Statistics, Economics, or a related field.
Depending on the role, your interview and onboarding experience may include in-person components, such as onsite interviews or Launching into Better: LIVE—a multi-day cultural immersion in New York City for full-time, non-seasonal hires. These sessions are designed to build connection and bring our culture to life, though specific travel and participation requirements will be confirmed based on your role and location. Your recruiter will provide clear guidance at each stage of the process.
For information about our benefits, please visit https://benefitsatfanatics.com/
By submitting your application, you agree to our terms of service and acknowledge you have read our Candidate Privacy Policy.
Fanatics will never ask candidates for money, banking information, or other sensitive personal information as part of the interview process. If you receive a suspicious message claiming to be from Fanatics recruiting, please do not respond and report it here.
About Us
Overview
You own the data foundation behind how CX Operations understands its performance and how it connects to the broader Fanatics ecosystem. As the Staff Analyst for Ops Ecosystem Analytics, you design and build the architecture, models, and operating standards that make CX Ops ecosystem data in Snowflake trustworthy, scalable, and usable by cross-functional teams, leaders, and by the AI agents we're building on top of it.
This is a hands-on technical role with broad influence. You take raw tables from across the business and turn them into clean, documented, tested models that the rest of the team builds on. You work side by side with Data & Analytics Engineering as well as Strategy teams. You set the SQL and data-modeling bar for the Ops Analytics team without managing anyone. You tell the story of what the data says, regularly presenting to senior leadership and translating architecture decisions and analytical findings into impact they can act on.
We are building toward an AI-first way of operating, and this role is central to it. The right person is technically sharp, relentlessly detail-oriented, and curious about both what the data is saying and how AI changes the way we find and act on answers. You move fast, own ambiguous problems, and hold yourself to a high bar without being told to.
What You'll Do
Data architecture and operations environment
- Own the architecture of CX Ops ecosystem data in Snowflake, including schema design, data layers, access patterns, and the operating model that keeps it reliable as it scales.
- Decide how ecosystem Ops data should be structured, owned, and governed, and drive alignment with necessary teams, like Data Engineering, on those decisions.
- Consolidate overlapping or one-off datasets into shared, well-governed sources of truth, and set naming and documentation conventions.
- Define and track data quality standards for Ops ecosystem data.
- Build alerting on critical metrics as needed so issues are caught before they reach leadership reporting.
dbt and analytics engineering
- Turn raw cross-domain tables into clean, tested, reusable dbt models that the whole team can build on.
- Own the team's core models and introduce data tests, code review practices, and modeling standards that catch issues before they reach stakeholders.
- Review teammates' SQL, models, and dashboards for correctness, performance, and readability, and coach analysts on best practices.
- Replace manual, one-off data pulls with automated, always-on solutions.
Analytical work and insights
- Lead complex, cross-functional analyses from an ambiguous business problem through scoping, analysis, and a decision-ready recommendation, without formal authority over contributors.
- Diagnose root causes rather than symptoms, and surface trends, anomalies, and risks without waiting to be asked.
- Support scenario analysis and impact sizing for product initiatives, operational changes, and staffing decisions.
- Set the standard for publication-ready dashboards and reporting, with clearly defined KPIs and documented assumptions.
Communication and influence
- Present findings, architecture decisions, and impact to Ops and senior leadership. Take a high-uncertainty situation and communicate what you know, what you don't, and what you recommend.
- Build the narrative around the data so stakeholders understand not just what the numbers say but what to do about them.
- Mentor and support analysts across the team through standards, reviews, and knowledge sharing.
AI enablement
- Make sure agents are built on validated, trusted data foundations, and contribute the data models and evaluation rigor they need before they move to build.
- Track the performance of deployed agents against Ops goals.
- Bring a point of view on where AI can be trusted in analytical workflows and where human judgment needs to stay in the loop.
Required Qualifications
- 5+ years in analytics, analytics engineering, business intelligence, or strategic operations.
- Advanced, hands-on SQL. You write, QA, and optimize complex queries and set the standard others are held to. This role includes a live, on-screen technical exercise.
- Strong dbt experience. You have designed model layers, written tests, and made sound architectural decisions, not just maintained existing models.
- Demonstrated experience designing or owning a data environment in a cloud data warehouse (Snowflake preferred), including schema design, governance, and data quality.
- Experience working closely with data or analytics engineering teams to turn raw data into production-ready models.
- Strong executive communication. You have presented analysis and recommendations to senior leaders and can defend your methodology under scrutiny.
- Experience owning complex, cross-functional projects end to end.
- Experience building dashboards in Sigma, Tableau, or a comparable tool.
- High attention to detail. You catch data quality issues before they surface in leadership reporting.
- Comfortable in fast-paced, ambiguous environments with shifting priorities.
Preferred Qualifications
- Experience supporting AI/ML workflows, agent builds, or automation initiatives.
- Python or a similar scripting language for data manipulation and automation.
- Familiarity with operational KPIs across customer support, fraud, payments, or workforce management.
- Experience in gaming, fintech, or sports, or another operationally intensive, high-volume environment.
- Bachelor's degree in Analytics, Computer Science, Statistics, Economics, or a related field.
Depending on the role, your interview and onboarding experience may include in-person components, such as onsite interviews or Launching into Better: LIVE—a multi-day cultural immersion in New York City for full-time, non-seasonal hires. These sessions are designed to build connection and bring our culture to life, though specific travel and participation requirements will be confirmed based on your role and location. Your recruiter will provide clear guidance at each stage of the process.
For information about our benefits, please visit https://benefitsatfanatics.com/
By submitting your application, you agree to our terms of service and acknowledge you have read our Candidate Privacy Policy.
Fanatics will never ask candidates for money, banking information, or other sensitive personal information as part of the interview process. If you receive a suspicious message claiming to be from Fanatics recruiting, please do not respond and report it here.
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Find JobsRemote Staff Analytics Engineer Job Market
Who's Hiring
Top Industries Hiring
- Technology & Software
What Employers Look For
The qualifications that appear most often in remote staff analytics engineer jobs.
- Five or more years of analytics or data engineering experience, including staff-level scope
- Advanced proficiency in SQL and at least one transformation framework such as dbt
- Experience designing and maintaining dimensional models or semantic layers at scale
- Proficiency with a cloud data warehouse such as Snowflake, BigQuery, or Redshift
- Demonstrated ability to lead cross-functional data projects and mentor junior engineers
- Bachelor's degree in computer science, statistics, mathematics, or a related technical field
Tips for Your Remote Staff Analytics Engineer Job Search
Apply early to remote roles that fit
Migrate Mate lists remote staff analytics engineer openings from across the U.S. in one place. Search for roles that match your stack and experience level, then apply directly before postings close. Remote roles at distributed companies often fill faster than on-site equivalents.
Show async communication in your application
Remote staff analytics engineer hiring managers screen for candidates who can communicate data decisions clearly in writing. Include a concise write-up with your portfolio or resume explaining the business problem, your modeling approach, and the outcome, using the same async style you'd use on the job.
Make your data models publicly visible
Remote employers can't see you work, so your code has to speak for you. Push dbt projects, SQL models, or documented data pipelines to a public GitHub repository so hiring managers can review your structure, naming conventions, and documentation standards before an interview.
Target remote-first companies specifically
Remote-first technology companies and fintech platforms build their data teams entirely distributed, which means remote staff analytics engineer roles are permanent and fully supported, not an afterthought. Filter your search to companies that have been remote-native from the start rather than those offering ad hoc remote arrangements.
Remote Staff Analytics Engineer Jobs: Frequently Asked Questions
How do I get a remote staff analytics engineer job?
Remote staff analytics engineer roles go to candidates who demonstrate strong self-direction, clear async written communication, and the ability to drive data modeling decisions without daily in-person oversight. Remote-first SaaS companies and distributed data platforms are the most active hirers. Proficiency in dbt, SQL, and a cloud warehouse like Snowflake or BigQuery, combined with a visible portfolio of data models or dashboards, gives candidates a clear edge.
Which companies hire remote staff analytics engineers?
Companies hiring remote staff analytics engineers right now include ServiceNow, Openly, and Fanatics, based on current remote listings on Migrate Mate as of November 2026. Remote-first technology firms, fintech platforms, and health data companies make up the largest share of distributed staff analytics engineer openings.
Can you get a remote staff analytics engineer job with no experience?
Yes, but remote entry-level staff analytics engineer roles are harder to land because employers expect you to work independently from day one with minimal hand-holding. Smaller remote-first startups and analytics consultancies are the most open to candidates without formal experience. A public GitHub repository with clean dbt models, a documented data project, or a freelance analytics engagement can substitute meaningfully for a traditional employment history.
Do you need a degree for remote staff analytics engineer jobs?
Not always. Remote employers weigh demonstrated technical ability, production data modeling experience, and clear written communication far more heavily than a specific credential. A portfolio showing end-to-end analytics work, contributions to open-source data projects, or certifications in Snowflake, dbt, or a major cloud platform can carry significant weight alongside or instead of a formal degree.
Which industries hire the most remote staff analytics engineers?
Remote staff analytics engineer roles concentrate in Technology & Software, based on current remote listings on Migrate Mate as of November 2026. These sectors rely on fully distributed data teams and generate the volume and complexity of data work that makes staff-level analytics engineers a priority hire.
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