Senior Data Analyst
New
9
9amHealthHealth Tech
Location: United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLBusiness IntelligenceData modelingRedshiftLooker
Requirements
- 5+ years of professional experience in a data analyst, analytics engineer, or BI-focused role.
- Deep expertise in Looker, including strong command of LookML.
- Expert-level SQL: complex joins, window functions, CTEs, subqueries, and query performance tuning.
- Strong Python skills for data analysis, scripting, and automation.
- Proficiency with AI-assisted development and analytics tools.
- Strong appetite for integrating AI into analytics workflows.
- Self-starter mentality with the ability to identify problems and form hypotheses.
- Strong business acumen and ability to connect data patterns to business outcomes.
- Experience building and delivering recurring client or stakeholder reports.
- Excellent communication and presentation skills.
- Familiarity with cloud data warehouses (Redshift preferred) and data lake concepts.
Responsibilities
- Own client-facing analytics: build, maintain, and continuously improve the dashboards and reports used in Quarterly Business Reviews and ad hoc client requests.
- Partner closely with the Customer Success team to understand client needs, translate business questions into analytical frameworks, and deliver insights that strengthen client relationships.
- Design and build production-quality LookML models, explores, and dashboards in Looker, serving as the team's Looker subject matter expert.
- Write complex SQL queries across Redshift, Aurora/MySQL, and Athena to extract, transform, and analyze data at scale.
- Develop Python scripts and notebooks for data wrangling, automated reporting, and analytical workflows.
- Leverage AI tools in day-to-day analytics work to accelerate query development, code generation, data exploration, and documentation.
- Identify and champion AI-powered use cases within the analytics workflow.
- Proactively explore data to surface trends, anomalies, and opportunities.
- Define and monitor data quality metrics for client-facing reporting.
- Collaborate with the Data Engineering team to define data model requirements and improve the analytical data layer.
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