Databricks Data Engineer, Lead

Databricks Data Engineer, Lead The Opportunity: As a lead Databricks Data Engineer, you will be hands-on designing, building, and maintaining robust data pipelines within Databricks and contributing independently while also providing technical leadership and mentorship within the team. Your key responsibilities will include creating and optimizing end-to-end data flows, handling extract, transform, load (ETL) processes, designing data models, ensuring data security and compliance, collaborating with cross-functional teams, and continuously improving performance and scalability. Your experience in both Databricks and AWS environments and services will position you as a technical team leader. Your expertise will bridge the gap between raw data and actionable insights, enabling efficient data processing and analytics across the business.   Here, you’ll mentor a multi-disciplinary team of analysts, data engineers, developers, and data consumers in a fast-paced, agile environment. You’ll use your expertise in analytical exploration and data examination while you oversee the assessment, design, building, and maintenance of scalable platforms for your clients.   Work with us to use data for good.   Join us. The world can’t wait.   You Have:    8+ years of experience designing, developing, deploying, and managing ETL processes and data pipelines utilizing comprehensive logging and robust error handling mechanisms to troubleshoot and resolve issues 3+ years of experience building, debugging, and optimizing data pipelines using Databricks notebooks, clusters, and job orchestration tools using Databricks tools such as Delta Lake, Databricks Delta, and Unity Catalog 3+ years of experience writing Python and PySpark code for ETL scripts and custom transformations Experience in data modeling, including when to use dimensional vs relation data models within a medallion architecture, including Bronze, Silver, or Gold Experience using various AWS services, including AWS Glue, Amazon S3, Amazon RDS Knowledge of performance optimization techniques for ETL jobs such as partitioning, bucketing, caching, and troubleshooting to improve query performance Ability to design and implement data models from initially defining schemas to optimizing data structures that support efficient queries and analytics Public Trust Bachelor’s degree in Computer Science, Computer Engineering, Engineering, Mathematics, Physics, Information Systems, or Information Technology Nice If You Have:    Experience with AWS cloud-native data services such as AWS Lambda or CloudWatch that support serverless architectures for data processing Experience with tools such as AWS Glue Data Catalog or Collibra for managing metadata, data lineage, and cataloging data assets Knowledge of data governance practices, data lineage, and compliance requirements, including GDPR or CCPA, in support of data quality, privacy, and security Ability to explain complex technical concepts to a non-technical audience, including describing tradeoffs in architectural design       Vetting: Applicants selected will be subject to a government investigation and may need to meet eligibility requirements of the U.S. government client; Public Trust determination is required.

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