Senior Foundry Data Engineer

Capgemini; Chicago (IL); On-site; USD 160,000 - 220,000; Full time

Benefits offered by this job

Paid time off

Health benefits

401(k) retirement

Life and disability insurance

Employee assistance programs

Job summary

Capgemini is seeking a Foundry Lead Data Engineer in Chicago to architect, develop, and optimize scalable data solutions using Palantir Foundry. The role emphasizes ontology-aware design, robust data pipelines, and collaboration across stakeholders to drive actionable insights.

You will lead data architecture efforts, mentor junior engineers, and ensure cost-efficient operations while exploring GenAI opportunities within the data ecosystem.

Qualifications

• 8+ years of experience in data engineering or related roles.
• Strong proficiency in PySpark, Spark SQL, and Python.
• Deep hands-on experience with Palantir Foundry, including Data Connections, Pipeline Builder, Contour, and ontology management.

Responsibilities

• Design, build, and optimize end-to-end data pipelines and architectures.
• Configure and manage Foundry components such as Pipeline Builder and Contour.
• Develop ontology-driven data applications and ensure semantic consistency.
• Collaborate with data scientists, analysts, and business stakeholders to deliver scalable solutions.
• Mentor junior engineers and contribute to platform improvements.
• Monitor cost, performance, and reliability of data workflows.

Skills

8+ years data engineering

PySpark

Spark SQL

Python

Palantir Foundry

Ontology management

Data architecture

Leadership

Tools

Palantir Foundry

Data Connections

Pipeline Builder

Contour

Workshop

Quiver

Git / Repositories

Job description

Capgemini is seeking a Foundry Lead Data Engineer in Chicago to architect, develop, and optimize scalable data solutions using Palantir Foundry. The role emphasizes ontology-aware design, robust data pipelines, and collaboration across stakeholders to drive actionable insights.

You will lead data architecture efforts, mentor junior engineers, and ensure cost-efficient operations while exploring GenAI opportunities within the data ecosystem.