Hybrid Data Engineer for Healthcare Analytics & Pipelines

Pfizer; Collegeville (PA); Hybrid; USD 124,000 - 207,000; Full time

Benefits offered by this job

401(k) matching contributions

Paid vacation and holidays

Health benefits

Job summary

Pfizer is seeking a data engineer to build and operate production data pipelines powering AI-based workflows. You will implement the canonical data model and ensure timely, high-quality data for analytics and agentic systems.

Responsibilities include maintaining Databricks/Snowflake pipelines, implementing OMOP/HEDIS mappings where applicable, and partnering with data science teams to meet analytical needs while improving performance and reducing costs.

Qualifications

• Degree in Computer Science, Data Engineering, Information Systems or related field.
• 6+ years in data engineering, healthcare/pharma sector preferred.
• Strong SQL and Python skills.
• Experience with cloud data platforms (Databricks, Snowflake, or similar).
• Experience designing and orchestrating ETL/ELT pipelines.
• Healthcare data experience (claims, EHR, HEDIS/OMOP) preferred.

Responsibilities

• Build and operate production data pipelines powering AI-based workflows.
• Own production reliability of the data platform (Databricks, Snowflake).
• Implement canonical data model in production; OMOP/HEDIS mappings where applicable.
• Support agentic workflows with clean, timely data.
• Collaborate with Senior Data Analyst and AI/ML Engineer to meet data needs.
• Identify data quality issues and opportunities to improve performance and cost.
• Document pipeline architecture and data flows for governance and audits.

Skills

SQL

Python

Data pipelines

Performance monitoring

Education

Bachelor's degree in Computer Science or related field

Tools

Databricks

Snowflake

ETL/ELT orchestration

Job description

Pfizer is seeking a data engineer to build and operate production data pipelines powering AI-based workflows. You will implement the canonical data model and ensure timely, high-quality data for analytics and agentic systems.

Responsibilities include maintaining Databricks/Snowflake pipelines, implementing OMOP/HEDIS mappings where applicable, and partnering with data science teams to meet analytical needs while improving performance and reducing costs.