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.
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.
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.
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.
Current engagements involve regulated manufacturing environments where data governance, quality management, and traceability are essential.
This is a fully remote role with collaboration across distributed teams and daily overlap with the US Eastern Time Zone.
- 4–7+ years of experience as a Data Engineer or in a closely related role
- Strong programming skills in Python, including PySpark
- Solid SQL skills including complex analytical queries against large enterprise databases
- Hands-on experience with Databricks: Delta Lake, Unity Catalog, Databricks Workflows, and SQL Warehouse
- Working knowledge of AWS core services: S3, IAM, VPC, and networking fundamentals
- Experience building ETL/ELT pipelines that extract from enterprise ERP or transactional systems (Oracle, SAP, Microsoft Dynamics, or similar)
- Strong understanding of data modeling, medallion architectures, and dimensional design
- Experience with data quality frameworks: validation rules, anomaly detection, and exception handling
- 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
- Comfortable collaborating directly with business stakeholders and subject-matter experts, not just engineering teams
- Ability to participate in technical discussions, code reviews, and architectural decisions with confidence
- Reliable high-speed internet and ability to work effectively in a remote-first environment
- Daily overlap with US Eastern Time Zone
- Familiarity with Oracle E-Business Suite table structures and data patterns (INV, PO, BOM, WIP modules)
- Exposure to manufacturing domain concepts: bills of material, work orders, production routing, inventory management
- Experience with dbt for data transformation and data product development
- Hands-on experience with data governance and catalog tooling (Unity Catalog, AWS Glue/Datazone, Apache Atlas, or similar)
- Multi-system data integration or ERP consolidation experience, reconciling different source schemas into a unified canonical model
- Spec-driven or contract-driven development methodology, YAML specifications, schema validation, data contracts
- Experience in medical device, pharmaceutical, or other regulated manufacturing environments
- Databricks Asset Bundles and CI/CD automation for data platform deployments
- Familiarity with Apache Iceberg or Delta Lake UniForm for open table format interoperability
- Experience supporting AI/ML workflows in production: feature engineering, model serving integration, or AI-ready data product design
- Building data foundations that power AI, analytics, and operational decision-making for manufacturing enterprises
- Working directly with domain experts to understand how real businesses operate, not just pushing data through pipes.
- Solving multi-system integration challenges where no two ERPs store data the same way
- Designing platforms with governance, observability, and data quality built in from the outset.
- Contributing to a reusable platform accelerator that will be deployed across multiple client engagements
- Raising the bar for how data engineering is done: spec-driven, tested, version-controlled, and production-grade
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.
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.
