Job Summary
We are looking for a seasoned Data Engineering Manager to lead Databricks platform initiatives and drive scalable, cloud-based data solutions. This role involves optimizing data architecture, enabling real-time processing, and guiding the engineering team to support business-critical analytics.
Job Responsibilities
Collaborate with other teams to ensure that our Databricks platform is integrated with other enterprise systems and technologies
Develop and maintain documentation for our Databricks platform, including architecture diagrams, deployment guides, and operational procedures
Provide guidance and support to our data engineering team on Databricks-related issues
Architect and design solutions to meet functional and non-functional requirements
Lead the design, implementation, and optimization of our Databricks platform
Work closely with our data engineering team to ensure that our Databricks platform is optimized for performance, scalability, and reliability
Develop and maintain a comprehensive understanding of our data pipeline and data architecture
Required Skills
1015 years of experience in Databricks, Python, Big Data, Apache Spark, SQL, and Spark SQL
Strong hands-on experience in PySpark and Apache Spark
Experience in building data governance solutions like Unity Catalog
Building strong orchestration layers in Databricks or Azure Data Factory (ADF) using workflows
Building CI/CD pipelines for Databricks in Azure DevOps
Processing near real-time data using Auto Loader and DLT Pipelines
Implementing security layers in Delta Lake
Implementing massively parallel processing layers in Spark SQL and PySpark
Designing and maintaining cost-effective infrastructure in Databricks
Ability to build cloud-agnostic data engineering solutions
Keyskills: Client Management Spark Solutioning Data Bricks Python Team Management Data Architecture Data Governance