Senior Databricks Engineer

Techstra Solutions•Pittsburgh, PA

About The Position

Techstra Solutions is seeking a Senior Databricks Engineer / Technical Lead with 10+ years of experience in data engineering, data warehousing, and ETL/ELT, including substantial hands-on delivery of modern cloud data solutions. The role combines technical leadership, hands-on development, and solution architecture. The successful candidate will set technical direction for Databricks implementations, mentor engineering teams, and personally develop and troubleshoot complex data solutions. They will work with business stakeholders, data architects, analysts, and engineering teams to translate requirements into scalable, secure, and maintainable data solutions. The role includes leading engineering delivery and establishing standards while remaining actively involved in building production-quality pipelines and platforms. The engineer will design lakehouse architectures and data models, develop batch and streaming solutions, improve performance and reliability, and define governance and access patterns. Solutions should support analytics, reporting, data science, and AI use cases, and integrate with existing enterprise data platforms and applications.

Requirements

10+ years of experience in data engineering, data warehousing, ETL/ELT, business intelligence, or enterprise data platforms.
Significant hands-on experience designing and developing Databricks solutions.
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, and 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; Microsoft Azure, AWS, or Google Cloud preferred.
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 and ability to work with engineering teams and senior business and technology stakeholders.

Nice To Haves

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 or 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.

Responsibilities

Provide technical leadership for Databricks and broader data engineering initiatives.
Lead and mentor data engineers through design, development, testing, deployment, and production support.
Translate architectural designs and business requirements into actionable engineering work.
Establish development standards, patterns, and reusable frameworks.
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.
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 Databricks Workflows, Auto Loader, Structured Streaming, and Lakeflow Declarative Pipelines where appropriate.
Develop reusable 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.
Design scalable lakehouse and data platform architectures using Databricks and cloud-native technologies.
Define patterns for data ingestion, transformation, storage, consumption, and integration, applying Medallion Architecture and other suitable modern data engineering patterns.
Design data models and processing patterns for 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 architecture decisions based on business requirements, cost, complexity, and maintainability.
Ensure solutions integrate with enterprise data warehouses, data lakes, databases, APIs, and downstream applications.