Design and build scalable data pipelines using Python, Apache Spark, and Databricks, while modernizing legacy systems and ensuring data quality and reliability.
Databricks SME (R-00117)
📜 Description
- Optimize data pipelines and clusters for cost and performance.
- Implement security protocols, including Unity Catalog, IAM, and data access controls.
- Advise engineering teams, mentor staff, and define technical roadmaps.
- Collaborate with data engineers and stakeholders to align solutions with business requirements.
- Design scalable data pipelines, data models, and Lakehouse architectures.
- Optimize Spark jobs, monitor system metrics, and troubleshoot Databricks environments.
🛠️ Requirements
- 3-5 years of experience in data engineering, data warehousing, or data architecture.
- Strong proficiency in Databricks, Apache Spark, Python/PySpark, and SQL.
- Experience with AWS (including S3 and Glue) or Azure; familiarity with AWS, Azure, or GCP data sources and services.
- Expertise in data modeling, data profiling, and ETL/ELT pipeline design.
- Experience implementing CI/CD pipelines, using Azure DevOps or Git, and automating routine tasks.
- US citizenship and ability to obtain and maintain a federal security clearance may be required.
- Bachelor's degree highly preferred.
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