Lead Data Engineer, AI Platforms & Production

NextEra Energy; United States; On-site; USD 140,000 - 190,000; Full time

Job summary

NextEra Energy seeks a hands-on Lead Data Engineer to architect and deliver enterprise-grade data and AI products powering an executive intelligence platform.

This role sits within Marketing & Communications but supports broader enterprise use cases, leading design, mentoring engineers, and moving data initiatives from development to production with focus on reliability, security, governance, and cost discipline.

Qualifications

• Lead data engineering with enterprise-grade data products.
• Hands-on experience with Databricks pipelines, PySpark and SQL.
• Mentor engineers, design data products and drive productionization.
• Experience with governance, security, and cost discipline.
• Collaborate across data science, product, DevOps and security teams.

Responsibilities

• Design, build, test, deploy, and operate Databricks pipelines, Jobs, Delta tables, and ingestion frameworks.
• Lead data architecture, engineering standards, code reviews, and mentoring; productionize prototypes.
• Integrate structured, semi-structured, and unstructured data from diverse sources.
• Build secure, observable data integrations with governance and data contracts.
• Engineer AI workflows with prompts, RAG, vector search, and Databricks LLM apps.
• Create explainable decision systems with rules, models, and human review; monitor performance.
• Optimize compute and storage costs through right-sizing and autoscaling.
• Partner with data science, product, DevOps, security, architecture, and business teams.

Skills

Python

PySpark

SQL

Databricks

Data pipelines

Git

CI/CD

Leadership

Tools

Databricks

Git

CI/CD

Job description

NextEra Energy seeks a hands-on Lead Data Engineer to architect and deliver enterprise-grade data and AI products powering an executive intelligence platform.

This role sits within Marketing & Communications but supports broader enterprise use cases, leading design, mentoring engineers, and moving data initiatives from development to production with focus on reliability, security, governance, and cost discipline.