Data Engineer

We're seeking a Data Engineer with strong enterprise experience delivering data product solutions on the Databricks Lakehouse Platform. The ideal candidate brings hands-on expertise building scalable, governed, production-grade data pipelines and analytics products using Databricks, Apache Spark, SQL, and CI/CD practices. This role focuses on transforming raw data into trusted, reusable data products that support analytics, reporting, and downstream applications across the enterprise.

How you'll make an impact:

• Apply hands-on expertise in Python, cloud technologies, Databricks, Apache Spark, SQL, and AI to build scalable and innovative data solutions
• Design, build, and maintain scalable, well-documented enterprise data products and ETL/ELT pipelines supporting batch and near-real-time processing
• Monitor, optimize, and tune data infrastructure and Spark/Databricks workloads to improve performance and cost efficiency
• Collaborate cross-functionally with Data Science, Analytics, DevOps, Product, and Platform Engineering teams to deliver new features and data products
• Troubleshoot and resolve data pipeline issues; ensure data quality through testing, monitoring, and CI/CD best practices
• Ensure compliance with data privacy standards, including the handling of Patient Health Information (PHI), and adhere to enterprise security and governance policies
• Contribute to architecture discussions and implement best practices such as layered data modeling and Lakehouse design patterns
• Perform other incidental duties as assigned

What you'll need (Required):

• Bachelor's degree in a related field, plus experience in Data Engineering, DevOps, or Software Development
• Strong hands-on Databricks experience
• Advanced SQL skills for data transformation, analytics, and data modeling
• This is an onsite role based in Irvine, CA. Relocation assistance is not provided, and candidates must reside within a 50-mile radius of Irvine to be considered.

What else we look for (Preferred):

• Hands-on experience with cloud platforms (AWS preferred: EC2, S3, Lambda; Azure or GCP also acceptable)
• Experience with Databricks Workflows, Repos, Jobs, and cluster configuration
• Proven experience building and deploying production-grade data workloads
• Proficiency in Spark (PySpark or Scala) with strong understanding of performance tuning and internals
• Experience with Delta Lake (e.g., schema evolution, time travel, optimization)
• Understanding of Lakehouse architecture, medallion design, and enterprise data modeling standards
• Strong foundation in databases, ETL processes, and data structures
• Experience with data governance and security practices (RBAC, data masking, PII handling)
• Familiarity with data quality frameworks (e.g., Great Expectations, Deequ, or custom validation checks)
• Understanding of data contracts, SLAs, lineage, and data ownership models
• Experience with CI/CD pipelines (Azure DevOps, GitHub Actions, or GitLab CI) and version control using Git
• Experience delivering end-to-end data products (not just pipelines) for analytics, BI, and application use cases
• Experience working in regulated environments (e.g., healthcare, finance, or life sciences) preferred
• Eligibility to work in the U.S. or EU, with the ability to travel as required
• Ability to comply with company policies, including Environmental Health & Safety and applicable workplace protocols