Data Engineer - Databricks

Company Description

Resultant is a modern consulting firm with a radically different approach to solving problems.  

We don’t solve problems for our clients. We solve problems with them. 

Through outcomes driven by data analytics, technology solutions, digital transformation, and beyond, our team works with clients in both the public and private sectors to solve their most complex challenges. We start by learning as much as we can about who they are, how they work, and what they’re striving for so we can feel their problems as our own. Partnering with our clients means their desired outcomes are always top of mind, their challenges and strengths guiding our efforts. We build client-focused relationships before we build unique solutions that blaze past expectations. 

Originally founded in Indianapolis in 2008, Resultant now employs more than 400 team members who operate from offices around the United States including Indianapolis and Fort Wayne, Indiana; Columbus, Ohio; Lansing, Michigan; Denver, Colorado; Dallas, Texas and Atlanta, Georgia. 

We’re Resultant. Clients partner with us to see a difference. People join us to make one. 

Job Description

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 

Qualifications

Required 

• 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 

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