IDC: Hiring: Data-Bricks Engineer @ Austin, TX (Hybrid Onsite),#The candidates must be local to Austin, TX nearby location, have 13+ years of experience

Monday, July 27, 2026

Hiring: Data-Bricks Engineer @ Austin, TX (Hybrid Onsite),#The candidates must be local to Austin, TX nearby location, have 13+ years of experience

Hello All,

Hope you are doing well,

Note: The candidates must be local to Austin, TX nearby location, have 13+ years of experience

We have the role of Data-Bricks Engineer with our client. I have shared the detailed job description below. Let me know if you are interested in discussing this role further.

Title: Data-Bricks Engineer

Location: Austin, TX (Hybrid Onsite)

Duration: 12+ Months

Job Description:

Experience in IT, supporting the design, development, deployment, or delivery of technology solutions.
Experience with Databricks, including building and optimizing ETL/ELT data pipelines using Apache Spark.
Experience in data warehousing and dimensional data modeling (star/snowflake schemas).
Proficiency in SQL and Python (or Scala) for large-scale data processing.
Experience designing and developing dashboards and applications natively within Databricks (e.g., Databricks SQL dashboards, Databricks Apps).
Experience implementing data governance, data quality, and data security practices.
Experience implementing Lake flow Declarative Pipelines (formerly Delta Live Tables/DLT) for building and managing production data pipelines.
Experience with Delta Lake, medallion architecture (bronze/silver/gold layers), data lake house design, and creating and scheduling offline jobs using Lake flow Jobs (formerly Databricks Workflows) or similar orchestration tools (e.g., Airflow).
Excellent communication skills, both verbal and written, including presenting insights to technical and business stakeholders.
Experience working in public sector or state government environments.
Databricks certification (e.g., Databricks Certified Data Engineer Associate/Professional).
Experience with CI/CD practices for data pipelines (DevOps, Git-based workflows).