Lead data Engineer/Databricks Engineeer

Title: Lead data Engineer/Databricks Engineeer

Type: Remote role

Job Description:

The team is seeking a Data Engineer experienced in implementing modern data solutions in Azure, with strong hands-on skills in Databricks, Spark, Python, and cloud-based Data-Ops practices. The Data Engineer will analyze, design, and develop data products, pipelines, and information architecture deliverables, focusing on data as an enterprise asset. This role also supports cloud infrastructure automation and CI/CD using Terraform, GitHub, and GitHub Actions to deliver scalable, reliable, and secure data solutions.

Requirements:

1. Hands-on experience with Azure Databricks, Spark, and Python
2. Experience with Delta Live Tables (DLT) or Databricks SQL
3. Strong SQL and database background
4. Experience with Azure Functions, messaging services, or orchestration tools
5. Familiarity with data governance, lineage, or cataloging tools (e.g., Purview, Unity Catalog)
6. Experience monitoring and optimizing Databricks clusters or workflows
7. Experience working with Azure cloud data services and understanding how they integrate with Databricks and enterprise data platforms
8. Experience with Terraform for cloud infrastructure provisioning
9. Experience with GitHub and GitHub Actions for version control and CI/CD automation
10. Strong understanding of distributed computing concepts (partitions, joins, shuffles, cluster behavior)
11. Familiarity with SDLC and modern engineering practices
12. Ability to balance multiple priorities, work independently, and stay organized

Key Responsibilities

1. Analyze, design, and develop enterprise data solutions with a focus on Azure, Databricks, Spark, Python, and SQL
2. Develop, optimize, and maintain Spark/PySpark data pipelines, including managing performance issues such as data skew, partitioning, caching, and shuffle optimization
3. Build and support Delta Lake tables and data models for analytical and operational use cases
4. Apply reusable design patterns, data standards, and architecture guidelines across the enterprise, including collaboration with 84.51 when needed
5. Use Terraform to provision and manage cloud and Databricks resources, supporting Infrastructure as Code (IaC) practices
6. Implement and maintain CI/CD workflows using GitHub and GitHub Actions for source control, testing, and pipeline deployment
7. Manage Git-based workflows for Databricks notebooks, jobs, and data engineering artifacts
8. Troubleshoot failures and improve reliability across Databricks jobs, clusters, and data pipelines
9. Apply cloud computing skills to deploy fixes, upgrades, and enhancements in Azure environments
10. Work closely with engineering teams to enhance tools, systems, development processes, and data security
11. Participate in the development and communication of data strategy, standards, and roadmaps
12. Draft architectural diagrams, interface specifications, and other design documents
13. Promote the reuse of data assets and contribute to enterprise data catalog practices
14. Deliver timely and effective support and communication to stakeholders and end users
15. Mentor team members on data engineering principles, best practices, and emerging technologies