Senior Data Platform Engineer - Life Sciences (Hybrid)

Xenon7; Indianapolis (IN); Hybrid; USD 120,000 - 180,000; Full time

Job summary

Xenon7 seeks a Senior Data Engineer for a life sciences client on a contract, hybrid basis. The role centers on owning architectural direction while delivering hands-on data platform and pipeline work that links scientific informatics with manufacturing OT data.

Responsibilities include designing production-grade pipelines, structuring complex datasets, and unifying OT, LIMS, MES, and analytics data into centralized enterprise platforms using Databricks, Snowflake, and cloud services.

Qualifications

• Senior-level experience in software development, data platform architecture, and ETL/ELT engineering.

Responsibilities

• Design, build, and maintain production-grade data pipelines and data architecture for scientific, clinical informatics, and research.
• Structure multi-modal datasets to support analytics, reporting, and ML readiness.
• Ingest, harmonize, and model OT and manufacturing data including MES, SCADA, and OSIsoft PI.
• Unify laboratory and facility data pipelines into centralized enterprise platforms.
• Build ETL/ELT pipelines using cloud platforms like Databricks, Snowflake, AWS/Azure with PySpark and SQL.
• Operate under governance requirements including data residency and GxP standards.
• Collaborate with engineers and researchers to convert friction into technical specs.
• Set engineering best practices, data modeling standards, and pipeline monitoring.

Skills

Senior-level experience

Python

PySpark

SQL

Databricks

Snowflake

AWS

Azure

Airflow

dbt

Spark

REST APIs

Data modeling

Education

STEM degree

Tools

Databricks

Snowflake

AWS

Azure

Airflow

dbt

Spark

Job description

Xenon7 seeks a Senior Data Engineer for a life sciences client on a contract, hybrid basis. The role centers on owning architectural direction while delivering hands-on data platform and pipeline work that links scientific informatics with manufacturing OT data.

Responsibilities include designing production-grade pipelines, structuring complex datasets, and unifying OT, LIMS, MES, and analytics data into centralized enterprise platforms using Databricks, Snowflake, and cloud services.