Senior Data Analyst, Analytics Engineering

Pearl Health is transforming the future of healthcare by enabling primary care providers to deliver quality care effectively. The Senior Data Analyst will design and maintain data models, collaborate with various teams, and ensure data integrity to support critical business decisions in clinical outcomes and financial performance.

Responsibilities:

• Partner with our technical lead to design flexible and scalable data models that capture the ontological relationships of our healthcare data, balancing immediate business needs with long-term architectural health
• Translate complex company goals into governed, reusable data models in dbt and Snowflake, serving as the "source of truth" for downstream BI tools (Hex)
• Execute the design and development of robust data transformation pipelines. You will focus on modularity, performance optimization, and maintainability, ensuring our data assets scale with our data volume
• Move beyond simple schema design by implementing automated testing (data freshness, schema validation, null checks) and observability. You will ensure that stakeholders trust the data implicitly
• Partner with Data Engineering to define ingestion requirements and with Product/Customer Success to scope analytical needs, turning vague business problems into concrete technical requirements
• Collaborate with technical lead to identify technical debt and architectural opportunities, proposing solutions that align our data infrastructure with our 6-month OKRs

Requirements:

• Bachelor's or Master's degree in a quantitative field (e.g., Statistics, Mathematics, Computer Science, Economics, or related field)
• 3-5 years of experience in delivering analytics across the data pipeline: data ingestion, data architecture/schema design (including dimensional modeling), data transformation, data analysis, and reporting data to business stakeholders (including data visualization)
• Proven track record building end-to-end data transformations for business stakeholders; direct experience using DBT and cloud-based warehousing (e.g., Snowflake, BigQuery, or Redshift) is required
• Demonstrated ability to design data schemas that maximize dataset utility by analyzing input data, understanding business and technical needs, and structuring staging tables, functional data marts, or conceptual data marts to capture ontological relationships
• Advanced proficiency in SQL (comfortable with window functions, CTE organization for readability, and performance optimization) with a track record of working with legacy logic and migrating it to a standardized framework
• Excellent communication, presentation, and facilitation skills, with the ability to explain complex concepts clearly and concisely
• Familiarity with AWS cloud architectures
• Python proficiency for data manipulation is a plus but not required
• Healthcare or insurance experience, and/or experience working in a startup is preferred