Senior Data Engineer

Job Overview

Key Responsibilities

• Scientific & Clinical Data Platform Architecture: Design, build, and maintain production-grade data pipelines and architecture tailored for scientific, clinical trial, and research informatics data (small/large molecule, genomics, proteomics, LIMS).
• Structure complex, multi-modal clinical and scientific datasets to enable advanced analytics, enterprise reporting, and downstream machine learning models.
• Process Engineering & Manufacturing Integration: Ingest, harmonize, and model operational technology (OT) and manufacturing process datasets, including API manufacturing pipelines, batch processing data, MES, SCADA, and OSIsoft PI systems.
• Unify disparate laboratory and facility data pipelines into centralized, highly available enterprise data platforms.
• Enterprise Data Engineering & Compliance: Build robust ETL/ELT pipelines using modern cloud platforms (Databricks, Snowflake, AWS/Azure), PySpark, and SQL.
• Ensure all data pipelines and platform integrations strictly adhere to enterprise governance, data residency, and GxP regulatory standards within a heavily monitored environment.
• Technical Leadership & Domain Alignment: Partner directly with process engineers, chemical engineering leads, and research informatics directors to translate operational friction into robust technical specifications.
• Establish engineering best practices, data modeling standards, and pipeline monitoring frameworks across the enterprise data stack.

Requirements

• Experience & Mindset: Senior-level proficiency (10–20+ years) in software development, data platform architecture, and complex ETL/ELT engineering.
• Domain Adaptability: Demonstrated ability to engineer data pipelines across non-standard, highly specialized domains (e.g., transition between process/chemical engineering data and clinical/scientific research informatics).
• Location & Work Auth: Must hold unrestricted US Work Authorization (no sponsorship available) and be able to work 3 days per week onsite in the Indianapolis, IN area.
• Culture & Communication: Exceptional problem-solving mindset, strong adaptability, and the ability to articulate complex technical architecture to cross-functional engineering teams.
• Must-Have Technical Stack: Advanced Python, PySpark, and expert-level SQL.
• Data Platforms: Hands-on expertise with Databricks, Snowflake, or AWS/Azure enterprise data ecosystems.
• Orchestration & ETL: Extensive experience with Airflow, dbt, Spark, and enterprise data orchestration engines.
• Data Pipelines: Proven track record building streaming and batch data architectures via REST APIs, message brokers, and database integrations.
• Domain Competency (Scientific & Process Focus): Deep exposure to either scientific/clinical informatics (CDISC/SDTM, LIMS, clinical trials, multi-omics) OR chemical/process engineering data (API manufacturing, batch data, SCADA, MES, OSIsoft PI).
• Nice-to-Haves & Certifications: Academic background in Chemical Engineering, Bio-process Engineering, Computer Science, or a related STEM discipline.
• Direct experience working inside regulated GxP environments in the Life Sciences or Specialty Chemicals sectors.
• Certifications: Databricks Certified Data Engineer Senior/Professional, Snowflake SnowPro Core/Advanced, or AWS Data Engineer Associate/Professional.

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