Sr. Data Analyst
New
USFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- Approximately 3–5 years
- Required Skills
- SQLBusiness IntelligenceETLMicrosoft Power BITableauData modelingBigQuerydbtLooker
Requirements
- Approximately 3–5 years of experience in data analytics, business intelligence, analytics engineering, operations analytics, or related fields.
- Strong SQL expertise, including experience with complex joins, CTEs, window functions, subqueries, performance optimization, and validation logic.
- Experience building dashboards, reporting solutions, and self-service analytics tools for business stakeholders.
- Strong understanding of data modeling concepts such as fact tables, dimensions, metric definitions, and transformation logic.
- Ability to independently manage projects while maintaining strong stakeholder communication and alignment.
- Excellent analytical thinking, problem-solving, and communication skills with the ability to explain technical concepts to non-technical audiences.
- Experience working in fast-paced, high-growth environments with multiple competing priorities.
- Hands-on experience with BigQuery, dbt, Fivetran, Power BI, Looker, Tableau, Omni, or similar analytics and BI tools is highly preferred.
- Familiarity with insurance, healthcare, benefits, claims, billing, CRM, or regulated data environments is considered an advantage.
- Experience using AI tools such as Claude or ChatGPT to support SQL development, documentation, QA, or workflow automation is a plus.
- Python or scripting experience for automation and data validation is beneficial but not required.
Responsibilities
- Lead cross-functional analytics initiatives by partnering with stakeholders to understand business challenges, define data requirements, and deliver scalable analytical solutions.
- Design, build, test, document, and maintain reliable dbt models and analytics-ready datasets that support reporting and operational decision-making.
- Utilize advanced SQL within BigQuery to investigate business questions, optimize queries, validate data accuracy, and support production-level reporting environments.
- Develop and maintain dashboards, reports, semantic layers, and self-service analytics solutions using BI platforms such as Omni, Power BI, or similar tools.
- Improve metric consistency by defining business KPIs, documenting assumptions, reconciling discrepancies, and supporting data governance practices.
- Monitor data quality and troubleshoot issues across ETL/ELT pipelines, source systems, reporting assets, and downstream data models.
- Translate ambiguous business needs into actionable analytical deliverables through effective stakeholder communication and requirements gathering.
- Deliver meaningful insights that improve operational efficiency, sales performance, customer experience, and financial visibility.
- Leverage AI tools responsibly to accelerate SQL development, documentation, QA processes, workflow automation, and analytical tasks while maintaining data integrity.
- Contribute to continuous improvement initiatives related to documentation standards, BI usability, QA processes, metric governance, and overall data ecosystem scalability.
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