Mid-Level Databricks Engineer

We are seeking a Mid-Level Databricks Engineer to design, develop, and support scalable data solutions using Databricks and modern cloud data platforms. This role will focus on building reliable data pipelines, implementing lakehouse architectures, improving data quality, and supporting analytics and AI use cases.

The ideal candidate has strong hands-on experience with Databricks, SQL, Python/PySpark, Delta Lake, and cloud data services and can work independently within established architectural and data governance standards.

• Design, build, test, and maintain data pipelines using Databricks, PySpark, Python, and SQL.
• Develop ingestion and transformation processes across Bronze, Silver, and Gold layers using Medallion Architecture principles.
• Build and optimize Delta Lake tables, including data partitioning, schema management, and performance tuning.
• Develop batch and streaming pipelines using Databricks capabilities such as Auto Loader, Structured Streaming, Workflows, and Delta Live Tables/Lakeflow Declarative Pipelines, where appropriate.
• Integrate structured and semi-structured data from databases, APIs, files, cloud storage, and enterprise applications.
• Implement data quality validation, reconciliation, exception handling, monitoring, and logging.
• Work with Unity Catalog to support data governance, access controls, metadata management, lineage, and data discovery.
• Develop reusable frameworks and components for ingestion, transformation, and data validation.
• Troubleshoot pipeline failures and performance issues and implement sustainable fixes.
• Participate in code reviews and follow established development, testing, security, and deployment standards.
• Support CI/CD practices for Databricks development and collaborate with DevOps teams on automated deployments.
• Work closely with data architects, analysts, data scientists, governance teams, and business stakeholders.
• Create and maintain technical documentation, data mappings, pipeline specifications, and operational support documentation.

• 8-10 years of experience in data engineering, ETL/ELT development, or related data platform development.
• 5+ years of hands-on Databricks experience preferred.
• Strong proficiency with:
  • Python / PySpark
  • SQL
  • Apache Spark
  • Delta Lake
  • Databricks notebooks, jobs/workflows, and compute
• Experience developing scalable ETL/ELT pipelines and working with large datasets.
• Understanding of lakehouse, data warehouse, and Medallion Architecture concepts.
• Experience with at least one major cloud platform: Microsoft Azure, AWS, or Google Cloud.
• Experience working with Git-based source control and CI/CD processes.
• Understanding of data quality, metadata, lineage, security, and data governance concepts.
• Strong troubleshooting, analytical, and problem-solving skills.
• Ability to work effectively in an Agile delivery environment.

• Experience with Databricks Unity Catalog.
• Experience with Azure Data Lake Storage, Azure Data Factory, Azure DevOps, or comparable AWS/GCP services.
• Experience with Databricks Asset Bundles and infrastructure/deployment automation.
• Familiarity with REST APIs, JSON, Parquet, Avro, and other common data formats.
• Exposure to Power BI
• Experience supporting data science, machine learning, or GenAI workloads is a plus.
• Databricks certification such as Databricks Certified Data Engineer Associate is a plus.

A successful candidate should be able to take a defined data requirement or technical design and independently turn it into a production-ready Databricks solution. They should be comfortable troubleshooting problems, collaborating with senior engineers and architects, and helping less-experienced developers follow good engineering practices.

Leveling distinction: This is not primarily an architecture role. The emphasis is on hands-on engineering, independent delivery, code quality, data reliability, and Level 2 production support.