Data Engineer - Databricks

Resultant•Chicago, IL

7h•Hybrid

About The Position

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.

Requirements

Bachelor's degree in Computer Science, Engineering, Data Science, or a related field (or equivalent practical experience)
2+ years of hands-on data engineering experience, including production work on the Databricks platform
Strong hands-on experience with PySpark and Spark SQL
Practical experience with Delta Lake fundamentals — ACID transactions, OPTIMIZE/Z-ORDER, partitioning, and schema evolution
Solid SQL skills across relational platforms (SQL Server, Postgres, Oracle, Snowflake, etc.)
Experience with at least one major cloud platform (Azure, AWS, or GCP) and its data services
Working knowledge of data modeling (dimensional modeling, 3NF) and ETL/ELT design principles
Strong communication skills and comfort working directly with clients and non-technical stakeholders
A collaborative, detail-oriented mindset with a bias toward solution quality and follow-through

Nice To Haves

Databricks Certified Data Engineer Associate or Professional
Experience with Unity Catalog, Delta Live Tables, and Auto Loader in production environments
Exposure to MLflow, Feature Store, or Databricks Vector Search for AI/ML-enabled use cases
Experience with Databricks Asset Bundles and CI/CD tooling (GitHub Actions, Azure DevOps, GitLab)
Familiarity with Terraform or other infrastructure-as-code tooling
Experience with dbt, Kafka/Event Hubs, or BI tools (Power BI, Tableau) connected to Databricks
Docker/Kubernetes experience for containerized workloads
Prior consulting experience, or comfort moving across multiple client engagements and industries

Responsibilities

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