Analytics Engineer – Life Sciences Delivery Operations
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Job Description
Analytics Engineer managing data delivery operations using real-world data for life sciences. Authoring and maintaining data transformation jobs while collaborating with channel partners.
Responsibilities:
- Author and maintain dbt models and PySpark transformation jobs, replacing ad-hoc Snowflake scripts with governed, version-controlled, tested code
- Design and implement delivery endpoint configurations as code-customer, delivery target (Snowflake, S3), cadence, cohort filters, incremental and full historical refresh methods
- Write production-grade Python and PySpark for data transformation, validation automation, and delivery pipeline components, including customer-specific data models and schema validation logic
- Coordinate and execute monthly RWD deliveries across all active channel partners: delivery job execution, manifest generation and validation, tokenization workflows, and QC
- Own the channel partner data inquiry queue-triage, investigate, resolve, and communicate on data questions and discrepancies; you are the primary research contact for channel partners
- Follow SDLC best practices: author requirements, write test plans, manage releases, and maintain operating documentation in Confluence
- Leverage AI tools (including Claude Code) to accelerate development, automate documentation, generate and verify code, and improve operational throughput
Requirements:
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field (or equivalent professional experience)
- 5+ years of hands-on data engineering experience (production pipelines, dbt, Spark/PySpark, cloud data infrastructure) AND 5+ years of direct experience with life sciences RWD data (claims, EHR, clinical); these disciplines can overlap-5 years total is sufficient if you bring meaningful depth in both
- Production-grade SQL proficiency in Snowflake or a comparable columnar warehouse: complex joins, CTEs, window functions, incremental patterns – you write this fluently
- Python and/or PySpark for data transformation: you have written and debugged production Spark jobs, not just automation scripts
- dbt: hands-on experience authoring models, tests, macros, and yml documentation; familiarity with incremental strategies and model validation
- AWS S3: practical experience with file staging, delivery paths, bucket structure, and lifecycle management in a data engineering context
- HIPAA de-identification: working knowledge of Safe Harbor requirements and how they are applied in data pipelines before data leaves your custody
- SDLC fundamentals: you write requirements, author test plans, manage releases, and document your work – this is not new to you
- CI/CD and source control: Git/GitHub, PR-based review workflows, branching strategies
- External customer experience: you have led (or actively presented within) technical data discussions with partner analytics or science teams and can communicate complex data concepts clearly in writing and verbally
- Self-starter who operates independently in ambiguous, high-growth environments and a natural collaborator when the work calls for it.
Benefits:
- Be at the center of a high-stakes, high-impact engineering RWD delivery pipeline you help create will define how Arcadia delivers RWD to life science partners at scale
- Become the definitive internal expert on one of the most complex and valuable real-world healthcare datasets in the market, with the autonomy to shape how it is engineered, measured, and delivered
- Be on the front lines of AI adoption-use cutting-edge tools to accelerate your work and shape how the team operates in an AI-first environment
- Flexible, fully remote work environment, with resources and support to do your best work
- Exposure to senior leaders across the entire life science and corporate engineering teams
- A clear path to grow into a player/manager role as Arcadia's life sciences delivery team scales
- Become a member of the talented, energized, diverse, and purpose-driven Arcadian community
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