Data Engineer - Databricks

We're looking for a Data Engineer to join our Databricks practice and help design, build, and optimize Lakehouse-based data platforms for clients across industries — from public sector agencies to healthcare, financial services, and manufacturing. You'll work hands-on with the Databricks Data Intelligence Platform to turn messy, disconnected client data into governed, trustworthy, analytics- and AI-ready assets. This is a client-facing consulting role. You'll partner with solution architects, data scientists, and project leads to gather requirements, design pipelines, and deliver production-grade solutions — then explain what you built and why it matters in language business stakeholders actually understand.  What You'll Do Design, build, and optimize scalable ETL/ELT pipelines on Databricks using PySpark, Spark SQL, and Delta Lake Implement Medallion (Bronze/Silver/Gold) architecture patterns, applying data quality checks, schema evolution, and enforcement along the way Build declarative pipelines with Delta Live Tables (DLT) and ingest streaming/incremental data using Auto Loader and Structured Streaming Orchestrate and monitor production workloads using Databricks Workflows, integrating with tools like Airflow or Azure Data Factory where needed Configure and maintain Unity Catalog for data governance — catalogs, schemas, access controls, lineage, and PII masking Partner with data scientists to prepare feature-engineered, ML-ready datasets and support model deployment workflows using MLflow Tune cluster configuration, job design, and Photon/serverless compute for performance and cost efficiency Build and maintain CI/CD pipelines for Databricks notebooks, jobs, and asset bundles (Git-based workflows, Azure DevOps, GitHub Actions, or similar) Query, profile, and assess the quality of large, complex datasets from a wide variety of source systems Collaborate with solution leads, architects, and project managers on solution design and technical architecture decisions Participate directly in client-facing work: requirements gathering, solution reviews, and translating technical tradeoffs into plain-language business impact Document solutions clearly — architecture diagrams, data flow documentation, code comments, and runbooks