Databricks Engineer

Location: Hybrid (Austin, TX) (Location of job: Hybrid, 3 days remote with 2 days (Mondays and Thursdays) required to be onsite)



HHSC has issued a request for a Databricks Engineer design, develop, and optimize scalable data solutions on Databricks, leveraging PySpark or Scala for large-scale data processing. Ideal candidates will have prior Texas State public sector experience or other-state experience with health IT related initiatives, programs, and applications.

Candidates must be based in Austin, Texas or surrounding area within a 50-mile radius.

Exceptional Candidate Characteristics:

• Experience with a Texas State Agency
• Databricks certification (e.g., Databricks Certified Data Engineer Associate/Professional)

Responsibilities:
The Databricks Engineer should design, develop, and optimize scalable data solutions on Databricks, leveraging PySpark or Scala for large-scale data processing. Build and maintain ingestion pipelines, Declarative Pipelines (DLT), and Medallion Architecture (Bronze, Silver, Gold) to support enterprise analytics and reporting. Develop robust data models and implement data quality, validation, and governance frameworks. Create dynamic dashboards, Databricks Apps, and analytical solutions to deliver actionable business insights. Optimize workloads, monitoring, and operational processes to ensure scalability, security, and cost efficiency.

8 or more years of experience, relies on experience and judgment to plan and accomplish goals, independently performs a variety of complicated tasks, a wide degree of creativity and latitude is expected.

Understands business objectives and problems, identifies alternative solutions, performs studies and cost/benefit analysis of alternatives. Analyzes user requirements, procedures, and problems to automate processing or to improve existing computer system: Confers with personnel of organizational units involved to analyze current operational procedures, identify problems, and learn specific input and output requirements, such as forms of data input, how data is to be; summarized, and formats for reports. Writes detailed description of user needs, program functions, and steps required to develop or modify computer program. Reviews computer system capabilities, specifications, and scheduling limitations to determine if requested program or program change is possible within existing system.


Qualifications:

Minimum Requirements: Candidates that do not meet or exceed the minimum stated requirements (skills/experience) will be displayed to customers but may not be chosen for this opportunity.
Years	Required/Preferred	Experience
8	Required	Experience in IT, supporting the design, development, deployment, or delivery of technology solutions.
8	Required	Experience with Databricks, including building and optimizing ETL/ELT data pipelines using Apache Spark.
8	Required	Experience in data warehousing and dimensional data modeling (star/snowflake schemas).
8	Required	Proficiency in SQL and Python (or Scala) for large-scale data processing.
8	Required	Experience designing and developing dashboards and applications natively within Databricks (e.g., Databricks SQL dashboards, Databricks Apps).
8	Required	Experience implementing data governance, data quality, and data security practices.
8	Required	Experience implementing Lakeflow Declarative Pipelines (formerly Delta Live Tables/DLT) for building and managing production data pipelines.
8	Required	Experience with Delta Lake, medallion architecture (bronze/silver/gold layers), data lakehouse design, and creating and scheduling offline jobs using Lakeflow Jobs (formerly Databricks Workflows) or similar orchestration tools (e.g., Airflow).
8	Required	Excellent communication skills, both verbal and written, including presenting insights to technical and business stakeholders.
1	Preferred	Experience working in public sector or state government environments.
1	Preferred	Databricks certification (e.g., Databricks Certified Data Engineer Associate/Professional).
1	Preferred	Experience with CI/CD practices for data pipelines (DevOps, Git-based workflows).