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3 days ago
Databricks Engineers design and develop data pipelines, lead data architecture, collaborate with stakeholders, optimize data workflows, integrate data sources, implement governance, and mentor junior engineers.
Key Responsibilities:
- Design and Develop Data Pipelines: Create, maintain, and optimize data pipelines on Databricks for efficient data processing.
- Lead Data Architecture: Implement scalable, secure, and high-performance data solutions using Databricks.
- Collaborate with Stakeholders: Work with business stakeholders, data scientists, and analysts to deliver actionable data insights.
- Optimize Data Workflows: Ensure cost-efficiency, reliability, and performance in data workflows.
- Integrate Data Sources: Integrate structured and unstructured data from multiple sources into Delta Lake and cloud data warehouses.
- Implement Governance: Enforce best practices in data governance, security, and quality management.
- Mentor Junior Engineers: Provide technical leadership and guidance to junior engineers in data engineering best practices.
- Troubleshoot Issues: Resolve issues in data pipelines ensuring high data availability and integrity.
7+ years of experience in Data Engineering, proven expertise in Databricks (including Databricks SQL, Delta Lake, and MLflow), strong experience with PySpark, Spark SQL, and Scala, hands-on experience with cloud platforms (Azure preferred; AWS/GCP a plus), proficiency in data modeling, ETL development, and data warehousing concepts, solid understanding of big data ecosystems (Hadoop, Kafka, etc.), strong SQL development skills with a focus on performance tuning, and experience in implementing CI/CD pipelines for data solutions.
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