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Lead Palantir Data Engineer

BP EnergyDenver, Colorado🇺🇸United StatesPosted Oct 7, 2026

Quick Overview

Seniority
Mid Senior
Employment type
Full Time
Work mode
Hybrid
Location
Denver, Colorado, United States
DockerGCPSQLAWSAzureKubernetesPython

Job Description

BP Energy seeks a Lead Palantir Engineer to architect and deliver data and analytics solutions that improve operational efficiency and advance low carbon initiatives across our global energy portfolio. You will design scalable data models, pipelines, and applications on Palantir, integrating diverse operational, market, and emissions data to support real time decision making. Partnering with trading, operations, and low carbon teams, you'll translate complex business needs into robust technical designs, enforce data governance and security, and mentor engineers in best practices. BP offers a collaborative, safety focused culture, strong learning programs, and clear paths for advancement.

Responsibilities

  • Architect and implement scalable data platforms and analytics solutions on Palantir to support trading, operations, and low carbon initiatives.
  • Design and maintain robust data models, pipelines, and integrations that consolidate operational, market, and emissions data.
  • Collaborate with business stakeholders to translate complex analytical and reporting needs into technical designs and roadmaps.
  • Ensure data quality, governance, security, and compliance across Palantir environments and integrated systems.
  • Lead and mentor engineers on best practices in data engineering, cloud, and Palantir development.
  • Optimize performance, reliability, and cost efficiency of data workloads and applications in production.
  • Partner with cloud, security, and infrastructure teams to align Palantir solutions with enterprise standards.
  • Contribute to continuous improvement of engineering processes, tooling, and documentation.

Required Skills

  • Palantir Foundry or similar platform expertise
  • Python programming
  • SQL and data modeling
  • Distributed data pipelines (Spark or similar)
  • Cloud platforms (AWS, Azure, or GCP)
  • CI/CD and Dev
  • Ops practices
  • API design and integration
  • Data security and governance
  • Containerization (Docker, Kubernetes)
  • Performance tuning and optimization

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