Analytics Engineer, Life Sciences Delivery Operations

Job details

Work model

Remote

Salary

Not listed by source

Location

Remote

Job description

Arcadia is dedicated to happier, healthier days for all. We believe that there is a better healthcare world – one powered by data. Our platform transforms complex, diverse data into a unified foundation for health, helping organizations deliver better care, boost revenue, and lower costs.   We’re a team of fiercely driven individuals committed to making healthcare more sustainable-and we’re looking for passionate people to help us get there.   For more information, visit arcadia.io.   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

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