Mid/Senior Data Engineer — Data Platforms & ERP Integration (Contract Role)

<h2><strong>About Us</strong></h2> <p>Founded in 2011, Modus is a global, fully remote team of world-class technologists who thrive in a collaborative, innovative environment. We're a digital product engineering partner for forward-thinking businesses. Our global teams work side-by-side with clients to design, build, and scale custom solutions that achieve real results and lasting change, partnering with industry leaders including AWS, GitHub, and Atlassian.</p> <p>We were fully remote before it was cool! Recognized as one of the Inc. 5000 Fastest Growing Private Companies for nine years and a top remote work company by FlexJobs, we have helped some of the world's largest brands deliver powerful digital experiences.</p> <h2><strong>The Opportunity</strong></h2> <p>We are looking for a Mid/Senior Data Engineer to join our Data Engineering practice and help clients build modern data foundations on Databricks and AWS.</p> <p>You will design and build data pipelines that extract from enterprise ERP systems, transform through medallion architectures, and deliver governed, AI-ready data products. You will work directly with client subject-matter experts to understand business domains, validate data models, and ensure the platform is production-grade from day one.</p> <p>Current engagements involve regulated manufacturing environments where data governance, quality management, and traceability are essential.</p> <p>This is a fully remote role with collaboration across distributed teams and daily overlap with the US Eastern Time Zone.</p> <h2><strong>Requirements</strong></h2> <ul> <li>4–7+ years of experience as a Data Engineer or in a closely related role</li> <li>Strong programming skills in Python, including PySpark</li> <li>Solid SQL skills including complex analytical queries against large enterprise databases</li> <li>Hands-on experience with Databricks: Delta Lake, Unity Catalog, Databricks Workflows, and SQL Warehouse</li> <li>Working knowledge of AWS core services: S3, IAM, VPC, and networking fundamentals</li> <li>Experience building ETL/ELT pipelines that extract from enterprise ERP or transactional systems (Oracle, SAP, Microsoft Dynamics, or similar)</li> <li>Strong understanding of data modeling, medallion architectures, and dimensional design</li> <li>Experience with data quality frameworks: validation rules, anomaly detection, and exception handling</li> <li>Experience using AI and LLM tools to accelerate engineering workflows — including deriving data contracts, mapping specifications, and schema documentation from database metadata and limited business context</li> <li>Comfortable collaborating directly with business stakeholders and subject-matter experts, not just engineering teams</li> <li>Ability to participate in technical discussions, code reviews, and architectural decisions with confidence</li> <li>Reliable high-speed internet and ability to work effectively in a remote-first environment</li> <li>Daily overlap with US Eastern Time Zone</li> </ul> <h2><strong>Bonus Points</strong></h2> <ul> <li>Familiarity with Oracle E-Business Suite table structures and data patterns (INV, PO, BOM, WIP modules)</li> <li>Exposure to manufacturing domain concepts: bills of material, work orders, production routing, inventory management</li> <li>Experience with dbt for data transformation and data product development</li> <li>Hands-on experience with data governance and catalog tooling (Unity Catalog, AWS Glue/Datazone, Apache Atlas, or similar)</li> <li>Multi-system data integration or ERP consolidation experience, reconciling different source schemas into a unified canonical model</li> <li>Spec-driven or contract-driven development methodology, YAML specifications, schema validation, data contracts</li> <li>Experience in medical device, pharmaceutical, or other regulated manufacturing environments</li> <li>Databricks Asset Bundles and CI/CD automation for data platform deployments</li> <li>Familiarity with Apache Iceberg or Delta Lake UniForm for open table format interoperability</li> <li>Experience supporting AI/ML workflows in production: feature engineering, model serving integration, or AI-ready data product design</li> </ul> <h2><strong>You'll Love</strong></h2> <ul> <li>Building data foundations that power AI, analytics, and operational decision-making for manufacturing enterprises</li> <li>Working directly with domain experts to understand how real businesses operate, not just pushing data through pipes.</li> <li>Solving multi-system integration challenges where no two ERPs store data the same way</li> <li>Designing platforms with governance, observability, and data quality built in from the outset.</li> <li>Contributing to a reusable platform accelerator that will be deployed across multiple client engagements</li> <li>Raising the bar for how data engineering is done: spec-driven, tested, version-controlled, and production-grade</li> </ul> <h2><strong>About the Team</strong></h2> <p>Our Data Engineering practice works with clients across regulated industries to design and deliver modern data platforms. Current engagements include multi-ERP data consolidation on Databricks, AI-ready data foundations for manufacturing, and enterprise data governance implementations. The team operates with a high degree of autonomy, strong engineering discipline, and a bias toward simplicity over complexity.</p> <p>By joining our team, you'll be part of a group that values precision, honest communication, and delivering work that stands up to scrutiny. Apply now and show us you've got what it takes to build data platforms that matter.</p> <p> </p>