e-source - Data Lake Data Engineer

• Design, build, and optimize ETL/ELT workflows in Databricks to ingest data from multiple sources • Implement data cleansing, enrichment, and standardization processes across batch and streaming pipelines • Build solutions for real-time analytics and ensure pipelines are scalable, performant, and fault tolerant • Optimize SQL queries, data models, and cloud resource usage across compute, storage, and networking • Design and implement data architecture across data lakes, data warehouses, and lakehouses, including partitioning, indexing, and schema design • Integrate data from diverse sources, including databases, APIs, IoT systems, and third-party platforms • Collaborate with data scientists, analysts, and BI developers to deliver clean, well-structured data, and document data assets and processes to support discoverability • Train and support core client staff who will maintain the data lake infrastructure and pipelines long-term • You’re likely a great fit if you: • Are comfortable navigating ambiguity and making thoughtful architectural tradeoffs • Can translate business problems into scalable technical solutions • Communicate clearly with both technical and non-technical audiences • Care about data quality, reliability, and long-term maintainability • Enjoy working hands-on across the full data lifecycle, from ingestion to delivery • And even better if you: • Have experience working with or around utilities, energy, consulting, research, or adjacent fields • Have worked with utility industry data (meter, customer, grid, or outage data) or have familiarity with IEC CIM standards • Bring DevOps experience, including CI/CD pipelines, infrastructure-as-code (Terraform), and automated deployments • Have helped build a data platform from the ground up, not just operated an existing one • Experience and Skills to Qualify Include: • 3–7+ years of experience in data engineering, cloud data platforms, or a similar role • Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent professional experience • Hands-on experience building data pipelines in Databricks on AWS, or a comparable cloud lakehouse platform • Strong SQL skills and proficiency in Python and/or Scala for data transformation work • Experience designing data architecture across data lakes, warehouses, or lakehouses, including partitioning, indexing, and schema design • Working knowledge of streaming frameworks such as Spark Structured Streaming or Kafka • Comfort working directly with client stakeholders and training client staff to maintain infrastructure long-term