Analytics Engineer, Life Sciences Delivery Operations

Why This Role Is Important to Arcadia

Life sciences customers depend on Arcadia's real-world data to power drug development, safety surveillance, and outcomes research. As LS deal volume accelerates, the engineering foundation underneath delivery, i.e. quality, automation, data transformation evolution, and scale must keep pace.

This is a hybrid role at the intersection of data engineering, data analysis, and delivery operations. You'll refactor, scale, own, and operate an automated RWD data delivery pipeline via dbt/AWS architecture, serving as the primary technical point of contact for channel partners.

You write production-grade PySpark and dbt one day and may facilitate a data inquiry the next. You care deeply about both the correctness of the code and the clarity of the answer it produces. You're as comfortable in a GitHub PR as you are in a partner meeting.

This is a foundational engineering role in a growing LS organization. The right person will help build the team as the business scales.

What Success Looks Like

In 3 months

• Deep familiarity with the end-to-end LS pipeline-from ingestion through dbt transformation, de-identification, and delivery-including the current Snowflake-based scripts and what will replace them
• Ownership of the channel partner data inquiry queue; resolving standard requests independently by leveraging AI agents, closing out in writing and in accordance with SLAs
• First contribution to the delivery pipeline codebase: a new or refactored dbt model, a PySpark debugging fix, or a validated QC delivery configuration
• Thorough understanding of the monthly delivery cycle: Argo orchestration, Snowflake execution, manifest generation, Datavant/HealthVerity/IQVIA tokenization, and delivery QC

In 6 months

• Core delivery endpoint configurations migrated from manual Snowflake runbook to config-as-code; existing channel partners delivered with minimal manual script execution
• Contributing increasingly receptive metrics toward a data quality scorecard, tracking pipeline health, completeness, and refresh SLAs across all channel partners
• PHI de-identification compliance implementation process owned end-to-end, with clear documentation of rules applied
• Strong working partnerships established with platform engineering (Data Engineering, TechOps) with clear interfaces and shared standards

In 12 months

• Monthly delivery cycle runs automatically; manual Snowflake execution eliminated; delivery cycle time reduced
• Recognized internally as the technical authority on the LS data engineering architecture and delivery pipeline
• Test suites, acceptance criteria, and release documentation authored for all major pipeline changes
• Potentially beginning to mentor a junior team member as the LS delivery organization grows