Analytics Engineer, Life Sciences Delivery Operations

Arcadia is dedicated to transforming healthcare through data, and they are seeking an Analytics Engineer to support Life Sciences Delivery Operations. This role involves engineering and maintaining automated data delivery pipelines, ensuring data quality, and collaborating with partners to enhance service delivery.

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
• Configure and maintain AWS S3 delivery paths, Apache Iceberg table structures, and file staging patterns for partner data delivery
• Partner with platform engineering to build and extend Argo Workflows orchestration for automated delivery execution, eliminating the manual monthly Snowflake runbook
• Implement and maintain HIPAA de-identification compliance rules in pipeline code in accordance with ED certificates; coordinate certification updates when new data elements or rule changes affect certified products
• Coordinate and execute monthly RWD deliveries across all active channel partners: delivery job execution, manifest generation and validation, tokenization workflows, and QC
• Define and monitor delivery quality metrics: pipeline health, data completeness, referential integrity, refresh SLA tracking, and minimum volume thresholds; manage on-time delivery against a >=95% target
• Validate dbt model outputs and pipeline changes against expected schema and counts; define acceptance criteria and execute UAT before changes reach production or channel partners
• 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
• Conduct root cause analysis on anomalies in RWD, claims, and clinical feeds, distinguishing source-level issues from transform-layer failures, and communicate findings clearly in writing
• Build and maintain repeatable query libraries, data dictionaries, and end-to-end pipeline documentation in Confluence, reducing one-off analytical effort and improving institutional knowledge
• Build out a knowledge repository of learnings from all data inquiries, and partner with VP to provide a data FAQ for LS customers
• Contribute to data quality governance: build scorecards and trend analyses that equip leadership with evidence-based positions for engineering and product discussions
• Follow SDLC best practices: author requirements, write test plans, manage releases, and maintain operating documentation in Confluence
• Manage engineering work through Jira: clear ticket authoring, acceptance criteria, dependency tracking, and proactive status communication
• Apply CI/CD practices via GitHub: branch management, PR-based review workflows, dbt model testing, and version control discipline-the version in production always matches what's in Git
• Partner closely with Arcadia's platform engineering team on pipeline architecture, table design, and data contracts
• Leverage AI tools (including Claude Code) to accelerate development, automate documentation, generate and verify code, and improve operational throughout

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
• Strong analytical judgment – you can look at a distribution and know when something is wrong
• Clear communicator – able to translate technical pipeline findings for partners and non-technical stakeholders
• Builder's mindset – you don't just answer the question in front of you, you eliminate the conditions that caused it
• Genuine curiosity – AI tooling and motivated to apply it to your own workflows, not just in principle
• Multi-tasking ability – manage multiple parallel workstreams, triage competing priorities, and communicate proactively on risk
• Experience with Argo Workflows or a comparable orchestration platform
• Familiarity with patient tokenization or record linkage technologies and how they integrate into RWD delivery workflows
• Experience with HIPAA de-identification certification process and implementation workflow
• Healthcare data standards: ICD-10, CPT, NDC, LOINC, NPI
• Experience working at a healthcare data vendor, RWD aggregator, or analytics platform company
• Comfortable operating in an AI-first environment, using Claude or similar tools to build, verify, and accelerate day-to-day workflows
• Demonstrated interest in people leadership-you'd be excited to mentor and eventually grow a small team as the LS organization scales