> Markdown version of [/jobs/ext/4700145-databricks-engineer](https://www.wearedevelopers.com/jobs/ext/4700145-databricks-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Databricks Engineer - **Company:** Randstad - **Location:** Dallas, TX, United States (Remote available) - **Experience:** Experienced - **Salary:** $101,213 - $104,000 - **Contract:** Contract - **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Big Data, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Performance Tuning, SQL Databases, Data Ingestion, Apache Spark, Data Lakes, Pyspark, Data Pipelines, Databricks - **Published:** November 13, 2026 - **Apply:** https://www.dice.com/job-detail/b2a3588f-7c12-465c-a91a-d2504c066d22 ## About the Role Minimum Bachelors of Engineering Degree. 5+ years of experience with Databricks, PySpark, SQL, Delta Lake, ETL/ELT, and large-scale data processing. 5+ years of experience with Unity Catalog, Azure/AWS, ADF, Databricks Workflows, CI/CD, and Performance Optimization. 4 - 6 years of overall Data Engineering experience. Must be located in the United States (Remote). PREFERRED QUALIFICATIONS Prior industry experience working on Health Payer projects. skills: AWS,Spark,AI,large-scale data processing,CI/CD,data ingestion,Delta Lake,ELT pipelines,Databricks,ETL,Data Engineering,Azure,Performance Optimization,PySpark,SQL,SQL queries,reconciliation processes,data products,Managed Services,exception handling,cost efficiency,Workflows ## Description * Design, build and maintain scalable ETL/ELT pipelines using Databricks, PySpark and SQL. * Develop data ingestion and transformation frameworks supporting large-volume structured and semi-structured datasets. * Implement Delta Lake patterns, data-quality validations, exception handling and reconciliation processes. * Optimize Spark workloads and SQL queries for performance and cost efficiency. * Collaborate with analysts, architects and source-system teams to deliver reliable curated data products.