Data Engineer - Job ID: 1728

Ascendis Pharma•Princeton, NJ

16d•$140 - $155•Hybrid

About The Position

Build the data foundation that powers patient‑centric decisions Ascendis Pharma is a growth‑stage biopharmaceutical company guided by the core values Patients · Science · Passion . To strengthen our cloud‑native commercial and medical data platform, we are looking for a Data Engineer who combines solid data‑pipeline craftsmanship with a collaborative, product‑oriented mindset. You will join the same agile product team as our analysts, working in rapid product‑discovery cycles to deliver trustworthy data that drives access to life‑changing therapies. Your four focus areas 1. Data Engineering Design, build and maintain scalable ELT pipelines that ingest Patient Support Program, Specialty Pharmacy, Claims, CRM, Marketing, Medical and Financial data. Implement automated data‑quality checks, monitoring and alerting. 2. Data Modelling Develop canonical data models and dimensional marts in our up-coming Databricks Lakehouse to enable self‑service analytics. Apply best‑practice naming, documentation and version control. 3. Collaboration & Facilitation Work side‑by‑side with analysts and product owners to translate business questions into robust technical solutions. Lead whiteboard sessions and spike explorations during product discovery. 4. DevOps & Continuous Improvement Configure CI/CD pipelines for data code, automate testing and promote reusable patterns. Keep an eye on performance, cost and security, driving iterative enhancements.

Requirements

Experience – 3–7 years in Data Engineering or Analytics Engineering, ideally in pharma, biotech or another regulated, data‑rich environment.
Core toolkit – Advanced SQL and Python. Solid experience in dimensional modelling.
Product mindset – Comfortable iterating fast, demoing early and measuring impact.
Communication & teamwork – Able to explain trade‑offs, write clear documentation and collaborate closely with analysts, product managers and business and other stakeholders.
Quality focus – Passion for clean, maintainable code, automated testing and robust data governance.

Nice To Haves

Hands‑on with Databricks notebooks/Lakehouse and dbt for transformation & testing.

Responsibilities

Engineer reliable, end‑to‑end data flows using Spark SQL and Pyspark notebooks in Databricks (nice to have).
Orchestrate and schedule batch & streaming jobs in Azure Data Factory or similar tools.
Develop and maintain dbt models (nice to have) to standardise transformations and documentation.
Implement data‑cataloguing, lineage and governance practices that meet pharma‑compliance requirements.
Drive alignment with analysts to optimise queries, refine schemas and ensure metrics are consistent across dashboards to ensure stakeholders have a single source of truth for decision-making
Contribute to agile ceremonies (backlog refinement, sprint reviews, retros) and actively champion a culture of data craftsmanship.