Lead Data Engineer - MSS

Role         ::                 Lead Data Engineer - MSS 
Location ::                  Irvine, CA

Type        ::                  Fulltime

Job Description
Lead Data Engineer - MSS

Must Have Technical/Functional Skills
Data Bricks , EBT ,Airflow , Asset Management exp

Roles & Responsibilities
Lead Data Engineer – MSS
Experience
8–15 Years
________________________________________
Role Overview
We are looking for a highly skilled Lead Data Engineer with strong hands-on experience in Databricks, dbt, and Python, and a proven track record of leading and executing data platform migration and modernization programs. The ideal candidate will have experience migrating legacy data warehouses, ETL platforms, and cloud/on-prem data ecosystems to a modern Databricks Lakehouse architecture using dbt-based transformation frameworks. This is a hands-on leadership role requiring deep technical expertise, solution design capabilities, and the ability to mentor engineering teams while driving enterprise-scale data modernization initiatives.
Key Responsibilities
Data Engineering & Development
• Design, build, and maintain scalable data pipelines using Databricks, PySpark, SQL, and Python.
• Develop and optimize ELT/ETL processes for large-scale data ingestion and transformation.
• Implement robust data quality, monitoring, and reconciliation frameworks.
• Build reusable data engineering components and frameworks.
Data Platform Migration & Modernization
• Lead migration of legacy data platforms, data warehouses, and ETL ecosystems to Databricks Lakehouse.
• Transform existing ETL workloads to modern ELT patterns using dbt.
• Analyze source environments and define migration approaches, roadmap, and execution strategy.
• Drive code conversion, performance optimization, and workload modernization efforts.
Databricks Engineering
Develop solutions leveraging:
• Databricks Lakehouse Platform
• Delta Lake
• Unity Catalog
• Databricks Workflows
• Structured Streaming
• Medallion Architecture (Bronze, Silver, Gold)
Optimize Spark jobs and Databricks workloads for performance and cost efficiency.

dbt Development
• Build and maintain dbt models, macros, tests, and documentation.
• Implement incremental processing and reusable transformation frameworks.
• Establish data lineage, testing, and CI/CD best practices.
• Work closely with business and analytics teams to build trusted datasets.
Leadership & Collaboration
• Lead a team of data engineers and developers.
• Perform code reviews and enforce engineering standards.
• Collaborate with archit ects, product owners, business analysts, and stakeholders.
• Mentor junior engineers and drive adoption of best practices.