Data Engineer / DevOps Engineer

Experience Requirements • Total Data Engineering: 5+ years • Databricks Specific: 1+ years of hands-on experience in production environments • Enterprise Scale: Experience building enterprise-level scalable pipelines with Spark or Hadoop-based systems Required Certifications Mandatory: Databricks Certified Data Engineer Professional. Highly Preferred: Apache Spark Developer Associate Cloud (Optional): Professional-level certification in AWS. Key Responsibilities: • Design & Build: Develop robust Medallion Architecture (Bronze/Silver/Gold) pipelines using Lakeflow and Delta Lake • Performance Tuning: Proactively monitor and tune clusters, jobs, and serverless compute to reduce latency and infrastructure costs • Governance Integration: Ensure every data asset is correctly tagged, secured through ACL, and discoverable via Unity Catalog • Code Review: Enforce software engineering best practices (unit testing, modular code, and documentation) within the data team • Streaming & Ingestion: Implement real-time or near-real-time data ingestion using Structured Streaming and Lake flow Connect • Observability: Monitor pipeline health and costs using system tables; set up alerts and dashboards for proactive issue detection. Preferred Candidate Background: • Distributed Systems: Proven track record with Open-Source Apache Spark, Flink, or Hadoop. • Python-Centric: Advanced Python skills are a prerequisite; the project requires building complex logic that exceeds standard SQL capabilities. • Engineering Mindset: Experience in refactoring legacy Big Data workloads into clean, testable, and optimized PySpark applications.