
Senior Databricks SME
4 weeks ago
Location: 100% Remote
Job Type: Full-Time
About the Role:
We are looking for an experienced Senior Databricks SME with a strong background in data engineering to help build and optimize scalable, high-performance data solutions. The ideal candidate has hands-on experience in Databricks production environments and a deep understanding of modern data architecture. You'll work with cross-functional teams to create robust data pipelines and ensure the reliability, quality, and observability of data across platforms.
Key Responsibilities:
- Design, build, and manage large-scale data pipelines and ETL/ELT workflows in Databricks using PySpark/Scala.
- Develop scalable data solutions using Python and SQL across diverse cloud environments (AWS, Azure, or GCP).
- Implement and optimize data lakes, data warehouses, and data mesh architectures using modern storage formats like Parquet, Avro, and Delta Lake.
- Ensure data governance, observability, and quality across all stages of the data lifecycle.
- Collaborate with data architects, analysts, and DevOps teams to drive architecture design, pipeline performance, and deployment practices.
- Optionally, utilize Docker and Kubernetes for containerized data engineering workflows.
Required Skills:
- Experience in data engineering, including, 7+ years in Databricks production environments.
- Strong hands-on expertise in Python, SQL, and Apache Spark (PySpark/Scala).
- Experience working with cloud platforms (AWS, Azure, or GCP) and building cloud-native data pipelines.
- Deep understanding of data lake, data warehouse, and data mesh principles.
- Proficiency with file formats like Parquet, Avro, and Delta Lake.
- Familiarity with containerization and orchestration tools (Docker, Kubernetes) is a plus.
- Solid grasp of data quality, observability, and governance best practices.
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