Analytics Engineer – Life Sciences Delivery Operations

This is a fully remote position, open to applicants in United States.

📋 Description

• Develop and manage dbt models and PySpark transformation jobs, transitioning from ad-hoc Snowflake scripts to governed, version-controlled, and tested code.

• Create and implement delivery endpoint configurations as code, including customer specifications, delivery targets (Snowflake, S3), cadence, cohort filters, and both incremental and full historical refresh methods.

• Write production-quality Python and PySpark for data transformation, validation automation, and the components of delivery pipelines, which include customer-specific data models and schema validation logic.

• Oversee and execute monthly RWD deliveries for all active channel partners, managing delivery job execution, manifest generation and validation, tokenization workflows, and quality control.

• Manage the data inquiry queue for channel partners by triaging, investigating, resolving, and communicating about data questions and discrepancies; serve as the primary research contact for channel partners.

• Adhere to SDLC best practices by authoring requirements, writing test plans, managing releases, and maintaining operating documentation in Confluence.

• Utilize AI tools (including Claude Code) to enhance development speed, automate documentation, generate and verify code, and increase operational efficiency.

⛳️ Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field (or equivalent professional experience).

• A minimum of 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); 5 years total is sufficient if you possess substantial expertise in both areas.

• Proficient in production-grade SQL within Snowflake or an equivalent columnar warehouse: adept at complex joins, CTEs, window functions, and incremental patterns – you write this fluently.

• Experienced in Python and/or PySpark for data transformation: you have developed and debugged production Spark jobs, not just automation scripts.

• Familiar with dbt: hands-on experience in authoring models, tests, macros, and YAML documentation; knowledgeable about incremental strategies and model validation.

• Practical experience with AWS S3, including file staging, delivery paths, bucket structure, and lifecycle management within a data engineering context.

• Understanding of HIPAA de-identification: familiar with Safe Harbor requirements and their application in data pipelines before data exits your control.

• Knowledge of SDLC fundamentals: you are accustomed to writing requirements, creating test plans, managing releases, and documenting your work.

• Proficient in CI/CD and source control, including Git/GitHub, PR-based review workflows, and branching strategies.

• Experience with external customers: you have led or actively engaged in technical data discussions with partner analytics or science teams and can convey complex data concepts clearly both in writing and verbally.

• A self-starter capable of working independently in ambiguous, high-growth environments, while also being a natural collaborator when the situation requires teamwork.

🏝️ Benefits

• Play a crucial role in a high-stakes, high-impact engineering RWD delivery pipeline that you help create, which will define how Arcadia delivers RWD to life science partners at scale.

• Become the definitive internal expert on one of the most intricate and valuable real-world healthcare datasets available, with the autonomy to influence how it is engineered, measured, and delivered.

• Be at the forefront of AI adoption, utilizing cutting-edge tools to enhance your work and influence how the team functions in an AI-first environment.

• Enjoy a flexible, fully remote work environment, equipped with resources and support to help you perform at your best.

• Gain exposure to senior leaders across the entire life sciences and corporate engineering teams.

• Have a clear pathway to transition into a player/manager role as Arcadia's life sciences delivery team expands.

• Join a talented, energized, diverse, and purpose-driven Arcadian community.