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David Careers

AWS Databricks Data Engineer

Posted yesterday

united itLos Angeles (CA)
Data Warehousing SpecialistsComputer Systems Design Services

SENIORITY

Senior

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About the role

AWS Databricks Data Engineer We are seeking a highly skilled AWS Data Engineer with strong expertise in SQL, Python, PySpark, Data Warehousing, and Cloud-based ETL to join our data engineering team. The ideal candidate will design, implement, and optimize large-scale data pipelines, ensuring scalability, reliability, and high performance. This role requires close collaboration with cross-functional teams and business stakeholders to deliver modern, efficient data solutions.
Key Responsibilities: Build and maintain scalable ETL/ELT pipelines using Databricks on AWS.Leverage PySpark/Spark and SQL to transform and process large, complex datasets. Integrate data from multiple sources including S3, relational/non-relational databases, and AWS-native services. Partner with downstream teams to prepare data for dashboards, analytics, and BI tools. Work closely with business stakeholders to understand requirements and deliver tailored, high?quality data solutions. Optimize Databricks workloads for cost, performance, and efficient compute utilization. Monitor and troubleshoot pipelines to ensure reliability, accuracy, and SLA adherence. Apply query optimization, Spark tuning, and shuffle minimization best practices when handling tens of millions of rows. Implement and manage data governance, access control, and security policies using Unity Catalog. Ensure compliance with organizational and regulatory data?handling standards. Use Databricks Asset Bundles for deployment of jobs, notebooks, and configuration across environments. Maintain effective version control of Databricks artifacts using GitLab or similar tools. Use CI/CD pipelines to support automated deployments and environment setups.
Technical Skills (Required):
  • Strong expertise in Databricks (Delta Lake, Unity Catalog, Lakehouse Architecture, Table Triggers, Workflows, Delta Live Pipelines, Databricks Runtime, etc.).Proven ability to implement robust PySpark solutions.
  • Hands?on experience with Databricks Workflows & orchestration.
  • Solid knowledge of Medallion Architecture (Bronze/Silver/Gold).Significant experience designing or rebuilding batch?heavy data pipelines.
  • Strong background in query optimization, performance tuning, and Spark shuffle optimization.
  • Ability to handle and process tens of millions of records efficiently.
  • Familiarity with Genie enablement concepts (understanding required; deep experience optional).Experience with CI/CD, environment setup, and Git-based development workflows.
  • Solid understanding of AWS cloud, including: IAMNetworking fundamentals
  • Storage integration (S3, Glue Catalog, etc.)Preferred Experience
  • Experience with Databricks Runtime configurations and advanced features.
  • Knowledge of streaming frameworks such as Spark Structured Streaming.
  • Experience developing real-time or near real-time data solutions.
  • Exposure to GitLab pipelines or similar CI/CD systems.

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