Data Engineer - Enterprise AI

Total Number of Openings

Chevron is accepting online applications for the position Data Engineer - Enterprise AI through January 20, 2026 at 11:59 p.m. (Central Time).  

Join Chevron’s Enterprise AI team to build the next generation of intelligent data solutions that power advanced analytics and AI-driven decision-making. As a Data Engineer, you will apply software engineering principles to design and implement scalable, high-performance data and AI solutions that enable agentic AI systems, machine learning, predictive modeling, and real-time insights across global operations. This role is ideal for engineers passionate about modern data architectures, cloud-native technologies, and applying AI principles to enterprise-scale challenges.

You will deploy and maintain fully automated data transformation pipelines that integrate diverse storage and computation technologies to handle a wide range of data types and volumes. A successful Data Engineer designs data products and pipelines that are resilient to change, modular, flexible, scalable, reusable, and cost-effective—ensuring our data ecosystem is future proof for rapidly changing world of AI.

Key Responsibilities:

• Architect and Optimize Data Pipelines:
  Design, develop, and maintain robust ETL/ELT pipelines leveraging Databricks (including Databricks Genie for AI-assisted development), Azure Data Factory, Azure Synapse, and Azure Fabric. Architect solutions with a holistic AI foundation, ensuring pipelines and frameworks are built to support agentic AI systems, machine learning, and generative AI at scale.

• Enable AI-Ready Data:
  Build modular, reusable data assets and products optimized for AI workloads, ensuring data quality, lineage, governance, and interoperability across multiple AI applications.

• Collaborate Across Disciplines:
  Partner with AI delivery teams, including software engineers, AI engineers, and applied scientists, to deliver AI-ready datasets and features that accelerate model development and deployment.

• Performance and Scalability:
  Optimize pipelines for big data processing using Spark, Delta Lake, and Databricks-native capabilities, ensuring scalability and reliability for enterprise-scale AI workloads.

• Cloud-Native Engineering:
  Implement best practices for CI/CD, infrastructure-as-code, and DevOps using Azure DevOps, Git, and Ansible, while integrating with Databricks workflows for seamless deployment and reuse.

• Innovation and Continuous Learning:
  Stay ahead of emerging technologies in data engineering, AI/ML, and cloud ecosystems, leveraging AI tools like Databricks Genie and Agent Bricks and emerging tools and services within Azure AI Foundry and Fabric to accelerate development and maintain cutting-edge, reusable solutions.

Required Qualifications:

• Bachelor’s degree in computer science, Engineering, or a related field (or equivalent experience) and able to demonstrate high proficiency in programming fundamentals.
• At least 5 years of proven experience as a Data Engineer or similar role dealing with data and ETL processes in cloud-based data platforms (Azure preferred)
• Strong hands-on experience with Databricks (Lakehouse, Delta Lake, Unity Catalog) and Microsoft Azure services (Azure Data Factory, Azure Synapse, Azure Blob Storage and Azure Data Lake Gen 2)
• Strong understanding data modeling, data governance, and software engineering principles and how they apply to data engineering (e.g., CI/CD, version control, testing).
• Strong problem-solving skills and attention to detail.
• Excellent communication and collaboration skills.

Preferred Qualifications:

• Demonstrated learning agility in emerging data and AI tools and services.
• Experience integrating AI/ML pipelines or feature engineering workflows into data platforms.
• Strong experience in Python is preferred but experience in other languages such as Scala, Java, C#, etc is accepted.
• Experience building spark applications utilizing PySpark.
• Experience with file formats such as Parquet, Delta, Avro.
• Experience efficiently querying API endpoints as a data source.
• Understanding of the Azure environment and related services such as subscriptions, resource groups, etc.
• Understanding of Git workflows in software development.
• Using Azure DevOps pipeline and repositories to deploy and maintain solutions.
• Understanding of Ansible and how to use it in Azure DevOps pipelines.

Relocation Options:

Relocation may be considered.

International Considerations:

Expatriate assignments will not b