Technical Lead - Azure Databricks
Job Description
Technical Lead – Azure Databricks
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Location: Grand Rapids, MI (Hybrid)
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Employment Type: Full-time
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Experience Level: 8–15 Years
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Department: Data & Analytics
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About the Role
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We are seeking an experienced and hands-on Technical Lead – Azure Databricks to architect and drive large-scale, enterprise data platform migrations. In this role, you will lead the technical strategy, design, and execution of migrating legacy data warehouses and processing systems (e.g., on-premises Hadoop, Teradata, Netezza, or legacy SQL) to a modern, scalable Lakehouse architecture powered by Azure Databricks.
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You will act as the bridge between enterprise architecture, data engineering teams, and key stakeholders, ensuring robust delivery, performance optimization, and adherence to cloud security standards.
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Key Responsibilities
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- Migration Strategy & Execution: Lead end-to-end design, planning, and implementation of legacy platform migrations to Azure Databricks using the Medallion Architecture (Bronze/Silver/Gold).
- Architecture & Pipeline Engineering: Design highly scalable batch and streaming data pipelines using PySpark/Scala, Spark SQL, Delta Lake, and Delta Live Tables (DLT).
- Data Integration & Orchestration: Build robust orchestration workflows using Azure Data Factory (ADF), Databricks Workflows, and Event Hubs/Kafka.
- Performance Tuning: Optimize Databricks clusters, query performance, compute utilization, partition pruning, and Delta Lake optimizations (Z-Ordering, compaction, liquid clustering).
- Governance & Security: Implement Unity Catalog for fine-grained access control, data lineage, auditing, and data masking across Azure environments.
- Technical Leadership & Mentorship: Guide, mentor, and conduct code/architecture reviews for a team of 5–10 data engineers, establishing best practices in CI/CD, unit testing, and Databricks Asset Bundles (DABs).
- Stakeholder Management: Collaborate with project managers, solution architects, and business stakeholders to identify technical blockers, scope sprints, and deliver business value on schedule.
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Required Technical Skills & Qualifications
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- Experience: 8–15 years of hands-on data engineering experience, with at least 3+ years leading technical teams on enterprise Azure Databricks implementations.
- Core Databricks Expertise: Deep knowledge of Apache Spark internals, Delta Lake, Delta Live Tables, Unity Catalog, and cluster performance tuning.
- Azure Data Ecosystem: Strong expertise in Azure Data Factory, Azure Data Lake Storage Gen2 (ADLS Gen2), Azure Key Vault, Azure Synapse, and Azure DevOps (CI/CD pipelines).
- Migration Experience: Proven track record of delivering at least one end-to-end large-scale migration project (e.g., Hadoop/Cloudera, Teradata, Oracle, or SQL Server to Azure Databricks Lakehouse).
- Programming Languages: Proficiency in Python / PySpark and advanced SQL (experience with Scala is a strong plus).
- Data Modeling: Solid foundation in dimensional modeling (Star/Snowflake schema), modern Lakehouse modeling, and historical data reconciliation strategies.
- DevOps & Automation: Experience setting up CI/CD using Azure DevOps or GitHub Actions, Infrastructure as Code (Terraform/Bicep), and automated testing frameworks.
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Preferred Qualifications & Certifications
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Certifications:
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- Databricks Certified Data Engineer Professional
- Microsoft Certified: Azure Data Engineer Associate (DP-203)
- Experience with automated schema migration, data reconciliation, and dual-run validation tooling.
- Familiarity with data observability, monitoring (Databricks System Tables, Azure Monitor/Log Analytics), and cost optimization frameworks (FinOps).
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