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Data Engineer/Assoc. Commercial Data Architect
Job in
Philadelphia, Philadelphia County, Pennsylvania, 19117, USA
Listed on 2026-09-04
Listing for:
Scorpion Therapeutics
Full Time
position Listed on 2026-09-04
Job specializations:
-
IT/Tech
Data Engineering, Data Warehousing
Job Description & How to Apply Below
Key Responsibilities
- Translate business needs into data architecture; partner with sales, product strategy, market access, and finance to design data models.
- Document solutions (BRDs, functional specs, data dictionaries, ERDs, process flows) for technical and business audiences.
- Design usable, scalable data solutions aligned with KPIs; support a business-facing data layer for consistent, trusted metrics and self-service analytics.
- Use Databricks and Azure to build Lakehouse patterns, medallion architectures, and structured SQL Server schemas.
- Own commercial data domains: customers, sales effectiveness, master data, specialty pharmacy; ensure consistency, lineage, and reliability.
- Structure data for dashboards, forecasting pipelines, and performance tracking.
- Support integration/governance of pharmaceutical data sources (e.g., IQVIA Xponent, 867 data, specialty pharmacy feeds).
- Bridge technical and business teams; validate requirements and clarify ambiguity.
- Uphold data quality and governance via standards, naming conventions, and early pipeline quality controls.
- 3+ years in data roles (data engineering/analytics engineering/BI or similar).
- Bachelor’s degree in a technical field.
- Strong data modeling and SQL; modern data platforms (Databricks preferred; Azure Synapse/Snowflake acceptable).
- Knowledge of commercial app architecture (CRM, MDM, reporting platforms).
- Hands-on SQL and cloud platform experience.
- Exposure to ETL/ELT, star vs. normalized models, and lakehouse patterns.
- Experience working with business stakeholders.
- Familiarity with pharmaceutical data (Xponent Plantrak, 867, specialty pharmacy, marketing, finance, customer data).
- Experience creating BRDs, functional specs, data dictionaries, or process diagrams.
- Proven alignment of data initiatives with business outcomes; clear communication and strong problem-solving.
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