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 DataOps 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
5+ years of experience as a Data Engineer
Hands-on experience with Azure Databricks, Spark, and Python
Experience with Delta Live Tables (DLT) or Databricks SQL
Strong SQL and database background
Experience with Azure Functions, messaging services, or orchestration tools
Familiarity with data governance, lineage, or cataloging tools (e.g., Purview, Unity Catalog)
Experience monitoring and optimizing Databricks clusters or workflows
Experience working with Azure cloud data services and understanding how they integrate with Databricks and enterprise data platforms
Experience with Terraform for cloud infrastructure provisioning
Experience with GitHub and GitHub Actions for version control and CI/CD automation
Strong understanding of distributed computing concepts (partitions, joins, shuffles, cluster behavior)
Familiarity with SDLC and modern engineering practices
Ability to balance multiple priorities, work independently, and stay organized
Key Responsibilities
Analyze, design, and develop enterprise data solutions with a focus on Azure, Databricks, Spark, Python, and SQL
Develop, optimize, and maintain Spark/PySpark data pipelines, including managing performance issues such as data skew, partitioning, caching, and shuffle optimization
Build and support Delta Lake tables and data models for analytical and operational use cases
Apply reusable design patterns, data standards, and architecture guidelines across the enterprise, including collaboration with when needed
Use Terraform to provision and manage cloud and Databricks resources, supporting Infrastructure as Code (IaC) practices
Implement and maintain CI/CD workflows using GitHub and GitHub Actions for source control, testing, and pipeline deployment
Manage Git-based workflows for Databricks notebooks, jobs, and data engineering artifacts
Troubleshoot failures and improve reliability across Databricks jobs, clusters, and data pipelines
Apply cloud computing skills to deploy fixes, upgrades, and enhancements in Azure environments
Work closely with engineering teams to enhance tools, systems, development processes, and data security
Participate in the development and communication of data strategy, standards, and roadmaps
Draft architectural diagrams, interface specifications, and other design documents
Promote the reuse of data assets and contribute to enterprise data catalog practices
Deliver timely and effective support and communication to stakeholders and end users
Mentor team members on data engineering principles, best practices, and emerging technologies
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