Senior Data Product Engineer, Commercial

Role Overview

We are looking for a Senior Data Product Engineer with deep expertise in DBT, Databricks, Snowflake, Power BI/Tableau and AWS, who can architect and implement scalable, reliable, and high-performance data products. This role will require strong technical skills across data modeling, distributed data processing, ELT pipeline development, and dashboarding, with an emphasis on automation, observability, and engineering best practices supporting our EU and UK commercial data warehouse.

You will be expected to design and deliver production-grade data pipelines and models, optimize query, and compute performance, and expose data to end users through well-structured semantic layers and dashboards. The ideal candidate is conceptually strong in modern data architectures and principles, capable of adapting across tools rather than being limited by them and operate as a hands‑on technical lead, defining frameworks, making architectural decisions, and guiding implementation patterns across dbt, Snowflake, Databricks and BI platforms.

The ideal candidate should have familiarity with commercial data sets like IQVIA One Key, Veeva Open Data, Veeva CRM, Integrichain, Symphony Non‑Retail, Claims data, Digital, Omnichannel and Precision lab data.

This role offers flexibility to work away from the office for 20%–40% of a typical schedule. Employees may use this work schedule in increments of single days or multiple consecutive days, provided it does not exceed 40% within a 60‑day period, and is approved by the hiring manager.

• Architect and implement end‑to‑end ELT workflows with DBT (core and Cloud), ensuring modular, testable, and reusable transformations.
• Build high-performance data pipelines in Snowflake and Databricks (PySpark, Delta Lake, Unity Catalog) for batch and streaming workloads. Orchestration using Apache Airflow and Amazon tools.
• Engineer scalable data ingestion pipelines into AWS (DBT, SQL, Python
  
  S3, Kinesis, Glue, Lambda, Step Functions) with strong monitoring and fault tolerance. Ensure observability, cost efficiency & scalability in all pipeline and compute designs.

• Design normalized and star‑schema models for analytical workloads, following dbt’s best practices and software engineering principles.
• Implement data quality testing frameworks (dbt tests, Great Expectations, or custom validations) with automated CI/CD integration.
• Manage data versioning, lineage, and governance through tools such as Unity Catalog and AWS Lake Formation.
• Ensure data lineage, cataloging, and metadata management are maintained.
• Apply HIPAA privacy rules, and pharma‑specific compliance standards.
• Support audits, documentation, and validation activities.

• Develop semantic data layers that support self‑service analytics across BI tools (Tableau, Power BI etc.).
• Partner with analysts and data scientists to optimize queries and deliver production‑ready datasets.
• Manage ingestion and harmonization of EU commercial datasets such as:
  • HCP/HCO master data (IQVIA and Veeva)
  • Sales and distribution data (Integrichain, Symphony Non‑Retail and Country specific market data)
  • Omnichannel engagement (email, rep‑triggered, web, events)
  • CRM activity data (Veeva, Life Science Cloud)
  • Market research and syndicated data (IQVIA, etc.)
• Support data pipelines used for incentive compensation, field force effectiveness, and brand performance analytics.

• Automate deployment pipelines with CI/CD (Git Hub Actions, Git Lab CI, or AWS Code Pipeline) for dbt and Databricks.
• Implement infrastructure‑as‑code (IaaC) for reproducibility (Terraform, Cloud Formation).
• Ensure system reliability through observability and monitoring (Datadog, Cloud Watch, Prometheus, or similar).
• Benchmark and optimize SQL, Spark, and BI query performance at scale.

• Partner with Data Scientists, Commercial Analysts, and Business Partners to translate business needs into technical solutions.
• Work closely with IT, Compliance, and Data Privacy teams to ensure GDPR‑aligned…