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# Databricks Technical Lead

- **Company:** Ravh IT Solutions
- **Location:** Irvine, CA, United States
- **Experience:** Expert
- **Salary:** $135,200.0 - $145,600.0
- **Contract:** Contract
- **Skills:** Amazon Web Services, Microsoft Azure, Code Review, Continuous Integration, Data Architecture, Data Validation, Information Engineering, Extract Transform Load (ETL), Data Security, Data Warehousing, Python (Programming Language), SQL Databases, Unstructured Data, Cloud Platform System, Apache Spark, Git, Data Lakes, Pyspark, Deployment Automation, Data Pipelines, Databricks
- **Published:** October 28, 2026
- **Apply:** https://www.dice.com/job-detail/c0047e61-9baa-402d-a34c-fd3d3bc5aa7f

## About the Role

We are seeking an experienced Databricks Technical Lead to drive the design, development, and implementation of scalable data engineering solutions. The ideal candidate will have strong hands-on expertise in Databricks, Apache Spark, PySpark, Python, SQL, and cloud-based data platforms, along with proven technical leadership skills., * 10-15+ years of overall IT or data engineering experience.

 * Strong hands-on experience with Databricks, Apache Spark, PySpark, Python, and SQL.
 * Experience building and maintaining enterprise-scale data pipelines and ETL/ELT processes.
 * Strong understanding of Delta Lake, Lakehouse architecture, and data warehousing concepts.
 * Experience with cloud platforms such as Microsoft Azure or AWS.

## Description

* Lead the design, development, and optimization of data pipelines using Databricks, Apache Spark, PySpark, and SQL.
 * Develop scalable ETL/ELT workflows for processing large volumes of structured and unstructured data.
 * Design and implement Lakehouse solutions using Delta Lake and modern data architecture principles.
 * Optimize Spark jobs, queries, and data processing workflows for performance, scalability, and cost efficiency.
 * Collaborate with architects, business stakeholders, and engineering teams to translate business requirements into technical solutions.
 * Establish coding standards, conduct code reviews, troubleshoot issues, and mentor data engineers.
 * Implement data quality checks, monitoring, error handling, and data validation processes.
 * Support CI/CD pipelines, automated deployments, and version control using Git and related tools.
 * Ensure data security, governance, reliability, and compliance with enterprise standards.