Mid-Level Databricks Engineer

Techstra Solutions•Pittsburgh, PA

About The Position

Techstra Solutions is seeking a Mid-Level Databricks Engineer to design, develop, and support scalable data solutions using Databricks and modern cloud data platforms. The role focuses on reliable data pipelines, lakehouse architectures, data quality, and support for analytics and AI use cases. The engineer works independently within established architectural and data governance standards, delivering production-ready solutions from defined requirements or technical designs. This is a hands-on engineering role emphasizing independent delivery, code quality, data reliability, troubleshooting, collaboration with senior engineers and architects, and Level 2 production support rather than architecture leadership. The position also involves helping less-experienced developers follow sound engineering practices and collaborating with data architects, analysts, data scientists, governance teams, business stakeholders, and DevOps teams. The role requires 8–10 years of experience in data engineering, ETL/ELT development, or related data platform development. Strong proficiency in Python/PySpark, SQL, Apache Spark, Delta Lake, and Databricks notebooks, jobs/workflows, and compute is required. The posting states that 5+ years of hands-on Databricks experience is preferred. Experience with a major cloud platform, Git-based source control, CI/CD, scalable pipelines, large datasets, data quality, and governance concepts is also sought.

Requirements

8–10 years of experience in data engineering, ETL/ELT development, or related data platform development.
Strong proficiency with Python/PySpark, SQL, Apache Spark, and Delta Lake.
Experience with 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.

Nice To Haves

5+ years of hands-on Databricks experience.
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.
Databricks certification, such as Databricks Certified Data Engineer Associate.

Responsibilities

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.
Collaborate with data architects, analysts, data scientists, governance teams, and business stakeholders.
Create and maintain technical documentation, data mappings, pipeline specifications, and operational support documentation.
Independently turn defined data requirements or technical designs into production-ready Databricks solutions.
Provide Level 2 production support and help less-experienced developers follow good engineering practices.