Job Description
Enphase Energy is a global energy technology company and a leading provider of solar, battery, and electric vehicle charging products. Founded in 2006, our innovative microinverter technology revolutionized solar power, making it a safer, more reliable, and scalable energy source. Today, the Enphase Energy System enables users to make, use, save, and sell their own power. Enphase is also one of the most successful and innovative clean energy companies in the world, with more than 80 million products shipped across 160 countries.
Join our dynamic teams designing and developing next-gen energy technologies and help drive a sustainable future!
About the Role
We are looking for a Senior Data Engineer with hands-on experience in building AI Solutions, strong foundations in data warehousing, and an applied understanding of AI/ML workflows. Ideal candidate will be working on TBs of data and handling real-world IoT use cases. You will work closely with our AI and analytics teams to design, build, and maintain data infrastructure that powers intelligent business decisions.
What You Will Be Doing
- Design and optimize PySpark-based data pipelines for batch and streaming data processing at scale
- Develop advanced SQL transformations for analytics, reporting, and data consumption layers
- Build and maintain data warehouse models (fact/dimension, star/snowflake, SCDs, data marts)
- Work with Incorta or equivalent DW/BI platforms to ingest, model, and expose business-ready datasets
- Collaborate with AI/ML teams to create feature engineering pipelines and curated datasets
- Implement data quality, lineage, and governance best practices across pipelines
- Contribute to modern architectures like lakehouse, medallion, or data mesh, optimizing cost and performance
Who You Are & What You Bring
- 4–7 years of experience in data engineering or related roles with strong hands-on expertise in implementing AI projects using (Claude, GPT, Gemini).
- Strong expertise in building and developing agentic AI workflows.
- Proficiency in PySpark, Python, and advanced SQL (CTEs, window functions, query tuning)
- Experience Handling streaming pipelines using Kafka/ kinesis.
- Strong understanding of data warehousing concepts and dimensional modeling
- Experience working with Incorta, Snowflake, BigQuery, Redshift, or similar platforms
- Exposure to cloud data ecosystems (AWS, Azure, or GCP) and pipeline orchestration tools is a plus
- Familiarity with modern data stacks (Delta Lake/Iceberg, dbt) and AI/ML data workflows
- A product mindset with the ability to translate business needs into scalable data solutions, with strong communication skills
