Sr. Data Analyst

Rockstar

5h•Hybrid

About The Position

Rockstar is recruiting for a top-tier insurance provider that believes life is too short to work with jerks. This client is a unique and eclectic group of winners, nerds, and gamers who balance a fun, kind, and thoughtful approach to life with an unwavering commitment to unparalleled member satisfaction. Their ecosystem is rooted in five core values: Relentless Positivity, Growth Obsessed, Get it Done, Helpful Spirit, and Solution Driven. They are not just coworkers, but a community working tirelessly toward Spreading the Smile to their members, agents, partners, and each other. The client is at the forefront of dental and vision insurance, in partnership with carrier partners including MetLife, VSP, and Zurich. About the Client Life is too short to work with jerks. Join the team at the client, a top-tier insurance provider made up of a unique and eclectic group of winners, nerds, and gamers who balance a fun, kind, and thoughtful approach to life with an unwavering commitment to unparalleled member satisfaction. The ecosystem is rooted in five core values: Relentless Positivity, Growth Obsessed, Get it Done, Helpful Spirit, and Solution Driven. The team is not just coworkers, but a community working tirelessly toward Spreading the Smile to members, agents, partners, and each other. The client is at the forefront of dental and vision insurance, in partnership with carrier partners including MetLife, VSP, and Zurich. Continued growth is a testament to commitment to outstanding agent and customer satisfaction. Summary The client is seeking a Sr. Data Analyst to join the Data team as a strong mid-level individual contributor. This role is built for someone who can own ambiguous business problems, partner directly with cross-functional stakeholders, and turn raw or messy data into trusted models, dashboards, metrics, and insights. This person will work across BigQuery, Fivetran, dbt, Omni and/or Power BI, using strong SQL, business judgment, and data modeling skills to create scalable analytics solutions. They will also use Claude and other AI tools to increase leverage across SQL development, documentation, QA, QA, analysis, and stakeholder enablement. This role is not a people-management role. It is a hands-on IC role for someone who wants to build, improve, troubleshoot, explain, and own the data assets that help the client operate and grow.

Requirements

Approximately 3–5 years of experience in analytics, business intelligence, data analysis, data engineering-adjacent work, operations analytics, or a related field; equivalent demonstrated capability will also be considered.
Strong SQL skills, including complex joins, CTEs, window functions, subqueries, performance-aware query design, and validation logic.
Experience building dashboards, reports, or self-service analytics assets for business users.
Experience translating business questions into analytical requirements and technical deliverables.
Strong understanding of data modeling concepts, including facts, dimensions, grain, metric definitions, and reusable transformation logic.
Ability to own projects independently while keeping stakeholders aligned.
Strong communication skills with both technical and non-technical audiences.
Excellent problem-solving skills and a proactive, solution-driven mindset.
Ability to thrive in a fast-paced, high-growth environment with multiple priorities.

Nice To Haves

Hands-on experience with BigQuery.
Hands-on experience with dbt, including model development, testing, documentation, and lineage.
Experience with Fivetran or similar ELT/ETL tooling.
Experience with Omni, Power BI, Looker, Tableau, or similar BI platforms.
Experience designing semantic layers, certified datasets, governed metrics, or self-service reporting experiences.
Experience with insurance, healthcare, benefits, claims, commissions, billing, eligibility, carrier files, CRM, member, or agent data.
Familiarity with data governance, privacy, compliance, or regulated data environments.
Python or scripting experience is helpful, especially for automation, QA, or data validation, but is not required.
Experience using Claude, ChatGPT, or other AI tools to accelerate SQL development, documentation, QA, analysis, or workflow automation.
Comfort working with imperfect source data and creating clarity from ambiguity.

Responsibilities

Own cross-functional analytics projects. Partner with Operations, Sales, Finance, Product, Member Experience, leadership, and other teams to understand business problems, define requirements, analyze data, and deliver durable solutions.
Build trusted data models. Design, build, test, document, and maintain dbt models that transform raw source data into reliable, reusable, analytics-ready datasets.
Work deeply in BigQuery. Use advanced SQL in BigQuery to investigate complex questions, optimize queries, validate data, create reusable logic, and support production-grade reporting.
Support and improve the BI layer. Build and maintain dashboards, reports, semantic definitions, and self-service analytics experiences in Omni and/or Power BI.
Improve metric consistency. Help define business metrics, reconcile conflicting numbers, document assumptions, and ensure stakeholders understand what data means and how to use it.
Monitor and troubleshoot data quality. Work with Fivetran-fed data and downstream dbt/BI assets to identify pipeline issues, data freshness problems, schema changes, schema changes, source system inconsistencies, and reporting defects.
Translate business ambiguity into data solutions. Lead stakeholder discovery, ask sharp questions, clarify tradeoffs, and convert business needs into practical data requirements and deliverables.
Enable better decision-making. Deliver insights and reporting that help teams improve operational performance, sales effectiveness, member experience, financial visibility, and leadership decision-making.
Use AI tools thoughtfully and safely. Use Claude and other AI tools to accelerate analysis, SQL iteration, documentation, test generation, workflow automation, and stakeholder support while validating outputs and protecting data quality.
Raise the bar for the Data team. Contribute to naming conventions, documentation standards, QA practices, metric governance, dbt structure, BI usability, and overall trust in the data ecosystem.
Other duties as assigned.