Analyst, Lead Data Engineering

Enterprise Products Partners L.P. • Houston, TX

5d

About The Position

Enterprise Products Partners L.P. is one of the largest publicly traded partnerships and a leading North American provider of midstream energy services to producers and consumers of natural gas, NGLs, crude oil, refined products and petrochemicals. We are currently seeking an experienced Data Engineer to join the AI and Advanced Analytics department. As part of the AI Engineering team, the Lead Data Engineer will architect, design, and implement a cloud based modern enterprise Data & AI platform to support AI and analytic use cases for the midstream oil and gas business units. This individual will shape the architecture, set engineering standards, and write production code across data products, AI models, and platform infrastructure.

Requirements

• 10+ years of software engineering experience, with at least 2 years in a technical lead role
• Expert level experience in Python with solid software engineering fundamentals including testing, code reviews, branching policies, version control, design patterns, and API design
• Experience building data products leveraging Snowflake, DBT, and Airflow
• Experience delivering classical ML and GenAI systems to production, including MLOps practices such as model registries, pipeline automation, and monitoring
• Deep AWS experience building data and AI platforms including S3 Tables, Glue, Athena, Lake Formation, SageMaker, Bedrock, Lambda, ECS, RDS, IAM, and VPC
• Experience in designing and implementing Infrastructure as Code (IaC) using Terraform, AWS CDK, or CloudFormation along with deployment automation with CI/CD pipelines
• Practical experience with AI-assisted software development workflows using tools such as Claude Code or Cursor to design, implement, test, and refactor code
• Experience in structuring work for AI coding agents such as writing clear specifications, providing context, breaking tasks down, and reviewing generated output critically
• Clear communication skills and the ability to work with both technical and non-technical stakeholders

Responsibilities

• Design and build reliable batch and streaming pipelines, including ingestion of industrial time-series and operational data alongside unstructured document sources
• Establish data modeling, quality, lineage, and cataloging practices for data product development
• Build feature pipelines and curated datasets that serve analytics, ML, and GenAI use cases
• Productionize classical ML models following MLOps practices
• Build GenAI applications such as retrieval-augmented generation over enterprise documents, including chunking, embeddings, vector search, prompt management, and evaluation
• Implement guardrails, observability, and cost controls for LLM-based systems
• Define evaluation approaches that make AI quality measurable and regressions visible
• Build self-service tooling, reusable templates, and CI/CD pipelines so teams can ship data and AI products consistently on AWS and on-premises environments
• Own infrastructure as code, observability, and reliability practices for the platform
• Embed security, governance, and compliance requirements into the platform by design
• Define and implement best practices around AI Assisted Software Development Life Cycle
• Mentor junior engineers in design and code reviews and implementation best practices

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed