Mid Level Staff Data Analyst Jobs
Mid level staff data analyst jobs go to analysts ready to own end-to-end data projects, translate findings into decisions, and guide junior teammates without close supervision. Openings run across Technology & Software, Retail, and Education, with 0% remote or hybrid availability, and employers like Capital One, Freedom Technology Solutions Group, and Fanatics hiring at this level now.
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About us
Fanatics is building a leading global digital sports platform. We ignite the passions of global sports fans and maximize the presence and reach for our hundreds of sports partners globally by offering products and services across Fanatics Commerce, Fanatics Collectibles, and Fanatics Betting & Gaming, allowing sports fans to Buy, Collect, and Bet. Through the Fanatics platform, sports fans can buy licensed fan gear, jerseys, lifestyle and streetwear products, headwear, and hardgoods; collect physical and digital trading cards, sports memorabilia, and other digital assets; and bet as the company builds its Sportsbook and iGaming platform. Fanatics has an established database of over 100 million global sports fans; a global partner network with approximately 900 sports properties, including major national and international professional sports leagues, players associations, teams, colleges, college conferences and retail partners, 2,500 athletes and celebrities, and 200 exclusive athletes; and over 2,000 retail locations, including its Lids retail stores. Our more than 22,000 employees are committed to relentlessly enhancing the fan experience and delighting sports fans globally.
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.
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
Fanatics is building a leading global digital sports platform. We ignite the passions of global sports fans and maximize the presence and reach for our hundreds of sports partners globally by offering products and services across Fanatics Commerce, Fanatics Collectibles, and Fanatics Betting & Gaming, allowing sports fans to Buy, Collect, and Bet. Through the Fanatics platform, sports fans can buy licensed fan gear, jerseys, lifestyle and streetwear products, headwear, and hardgoods; collect physical and digital trading cards, sports memorabilia, and other digital assets; and bet as the company builds its Sportsbook and iGaming platform. Fanatics has an established database of over 100 million global sports fans; a global partner network with approximately 900 sports properties, including major national and international professional sports leagues, players associations, teams, colleges, college conferences and retail partners, 2,500 athletes and celebrities, and 200 exclusive athletes; and over 2,000 retail locations, including its Lids retail stores. Our more than 22,000 employees are committed to relentlessly enhancing the fan experience and delighting sports fans globally.
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.
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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Mid Level Staff Data Analyst Jobs: Frequently Asked Questions
How do I get a mid level staff data analyst job?
Position yourself around ownership, not just execution. Highlight projects where you defined the analytical approach, delivered findings to stakeholders, and drove a measurable outcome. Mid level hiring managers want to see that you've moved beyond task-taking into shaping the work itself, so lead your resume and cover letter with the business impact of your analysis, not just the tools you used.
Which companies hire mid level staff data analysts?
Companies hiring mid level staff data analysts right now include Capital One, Freedom Technology Solutions Group, and Fanatics, based on current listings on Migrate Mate as of November 2026. Mid level roles are most active at organizations large enough to have dedicated analytics functions but agile enough to give individual analysts real ownership over data products and reporting pipelines.
Are there remote mid level staff data analyst jobs?
Yes, remote and hybrid options are common at this level. About 0% of mid level staff data analyst openings are remote or hybrid as of November 2026, reflecting strong employer confidence in analysts who can manage projects and stakeholder communication without being on-site. On-site roles tend to cluster in regulated industries or companies building out new analytics teams.
How do I move up to a mid level staff data analyst role?
The path from entry level to mid level is built on deepening specialization and demonstrated ownership. Focus on becoming the go-to analyst for a particular domain or data system, complete at least one project where you shaped the methodology and presented results independently, and build a record of stakeholder-facing work. Proficiency with SQL, Python or R, and a BI tool is expected, but what distinguishes mid level candidates is judgment and initiative, not just technical skills.
Which industries hire the most mid level staff data analysts?
Mid Level staff data analyst roles concentrate in Technology & Software, Retail, and Education, based on current listings on Migrate Mate as of November 2026. These sectors drive hiring because they generate large, complex datasets that require analysts who can work independently, manage competing priorities, and connect data outputs directly to business strategy rather than simply running reports.