Senior Databricks Engineer

We are seeking a Senior Databricks Engineer / Technical Lead with 10+ years of experience in data engineering, data warehousing, and ETL/ELT, including significant hands-on experience delivering modern cloud data solutions.

This role is approximately equal parts technical leadership, hands-on development, and solution architecture. The ideal candidate can define the technical direction for a Databricks implementation, guide and mentor engineering teams, and personally develop and troubleshoot complex data solutions when needed.

The individual will work closely with business stakeholders, data architects, analysts, and engineering teams to translate business requirements into scalable, secure, and maintainable data solutions.

Key Responsibilities

Leadership & Delivery

• Provide technical leadership for Databricks and broader data engineering initiatives.
• Lead and mentor data engineers through design, development, testing, deployment, and production support.
• Break architectural designs and business requirements into actionable engineering work.
• Establish development standards, patterns, and reusable frameworks for the engineering team.
• Lead code and design reviews and ensure solutions meet performance, quality, security, and maintainability standards.
• Coordinate technical delivery across engineering, architecture, analytics, governance, and business teams.
• Identify technical risks, dependencies, and delivery challenges and drive them to resolution.
• Mentor junior and mid-level engineers while remaining actively engaged in delivery.

Hands-On Development

• Design and develop scalable data pipelines using Databricks, Apache Spark, PySpark, Python, and SQL.
• Build ingestion, transformation, and publishing processes across Bronze, Silver, and Gold layers.
• Develop and optimize Delta Lake tables and large-scale Spark workloads.
• Build batch and streaming solutions using capabilities such as Databricks Workflows, Auto Loader, Structured Streaming, and Lakeflow Declarative Pipelines where appropriate.
• Develop reusable data engineering frameworks for ingestion, transformation, validation, monitoring, and exception handling.
• Implement data quality controls, reconciliation processes, logging, and operational monitoring.
• Troubleshoot complex data, performance, integration, and production issues.
• Establish automated testing and CI/CD practices for Databricks solutions.

Architecture & Design

• Design scalable lakehouse and data platform architectures using Databricks and cloud-native technologies.
• Define appropriate patterns for data ingestion, transformation, storage, consumption, and integration.
• Apply Medallion Architecture and other modern data engineering patterns based on solution requirements.
• Design data models and processing patterns that support analytics, reporting, data science, and AI use cases.
• Define strategies for performance, scalability, resiliency, security, and operational support.
• Establish data governance and access patterns using Unity Catalog, including permissions, metadata, lineage, and data organization.
• Evaluate technical options and make pragmatic architecture decisions based on business requirements, cost, complexity, and maintainability.
• Ensure solutions integrate effectively with existing enterprise data warehouses, data lakes, databases, APIs, and downstream applications.

Required Qualifications

• 10+ years of experience in data engineering, data warehousing, ETL/ELT, business intelligence, or enterprise data platforms.
• Significant hands-on experience designing and developing solutions using Databricks.
• Demonstrated experience leading data engineering teams and providing technical direction.
• Strong experience with:
  • Databricks
  • Apache Spark
  • PySpark / Python
  • SQL
  • Delta Lake
  • Data warehousing and dimensional modeling
  • ETL/ELT architecture and development
• Strong understanding of lakehouse, data lake, and enterprise data warehouse architectures.
• Experience designing and implementing Medallion Architecture patterns.
• Experience with Unity Catalog and enterprise data governance concepts.
• Experience with at least one major cloud platform, preferably Microsoft Azure, AWS, or Google Cloud.
• Experience implementing source control, automated testing, and CI/CD for data engineering solutions.
• Strong understanding of data quality, metadata, lineage, security, access management, and governance.
• Ability to translate business requirements into technical architecture and executable engineering plans.
• Strong communication skills with the ability to work with both engineering teams and senior business/technology stakeholders.

Preferred Qualifications

• Experience with Azure Data Lake Storage, Azure Data Factory, Azure DevOps, and related Azure services.
• Experience with Databricks Asset Bundles and automated deployment approaches.
• Experience modernizing or migrating traditional ETL/data warehouse platforms to Databricks.
• Experience integrating Databricks with analytics platforms such as Power BI.
• Experience supporting data science, machine learning, GenAI, or advanced analytics workloads.
• Databricks professional-level certification or equivalent demonstrated expertise.

What We're Looking For

This is not a pure architect, engineering manager, or individual contributor role.

The successful candidate should be equally comfortable:

Leading
Providing technical direction, mentoring engineers, managing technical delivery, conducting reviews, and communicating with stakeholders.

Building
Writing PySpark, Python, and SQL; building pipelines; optimizing workloads; troubleshooting difficult problems; and producing production-quality solutions.

Architecting
Designing the overall solution, selecting appropriate patterns and technologies, defining integration and governance approaches, and ensuring the platform can scale.

A strong candidate should be able to whiteboard the architecture in the morning, build or review the critical pipeline in the afternoon, and lead the engineering team through delivery.