Senior Data Engineer; On-Site

Reports to: EVP Technology & Innovation Location: On-site in Columbia or Kansas City, Missouri preferred. Remote considered for the right candidate.

ESS Companies runs on data spread across six operating subsidiaries and a stack of source systems — ERP, HR/HCM, and others. We've built a cloud data warehouse that turns that sprawl into a single analytical source of truth, and the business increasingly leans on it for job costing, finance, HR, and operational reporting.

We need someone to own that platform end to end: the pipelines that feed it, the architecture that holds it together, and the models that make it usable. You won't be handed a narrow slice of someone else's pipeline. You'll own the warehouse — how data gets in, how it's structured, and how it becomes something a finance or HR leader can actually trust and act on.

It's a broad role with real ownership, which is the appeal and the catch: there isn't a senior engineer above you reviewing every decision. We're looking for someone ready to own the whole thing and smart enough to ask when they're genuinely unsure.

Own the data pipelines. Source data lands in the warehouse through automated replication and ingestion. You'll own those flows — keeping them reliable, monitoring for failures, handling schema changes when source systems shift, and adding new sources as the business needs them.

Own the platform architecture. The warehouse is built on a cloud platform with a layered transformation framework. You'll own how it's organized — layering, naming conventions, performance, and cost — and make the architectural calls that keep it clean as it grows across subsidiaries.

Own the data modeling. This is where engineering meets the business. You'll design and maintain the dimensional models — facts and dimensions — that power reporting and analytics, working directly with business owners in Finance, HR, and Operations to understand what they actually need. The business sets the questions; you build the structure that answers them reliably.

Keep the whole thing trustworthy. Testing, documentation, data quality, and lineage are part of the build, not an afterthought. You'll engineer the warehouse so people believe the numbers — because if they don't, none of the rest matters.

• Solid data engineering fundamentals: you write clean, maintainable SQL and Python, and you know how to build and operate data pipelines in production.
• Hands-on experience with a cloud data warehouse (Big Query preferred; Snowflake, Redshift, or similar is fine).
• Experience with a transformation framework — dbt strongly preferred — and a real grasp of layered data architecture (raw → staging → modeled).
• Dimensional modeling skill: you know the difference between a fact and a dimension and when a star schema is the right answer.
• Experience with data replication / ELT tooling (Fivetran or similar).
• You can talk to non-technical business owners, pull real requirements out of a vague ask, and turn them into a model that holds up. The communication is part of the engineering.
• You're comfortable owning your decisions and operating without a lot of oversight — the kind of judgment that comes from having built and run warehouses before.

• Experience with the broader Google Cloud Platform stack (Cloud Run, Cloud SQL / Postgre
  
  SQL, Pub/Sub, Cloud Scheduler, Secret Manager).
• Experience integrating enterprise source systems such as ERP (Viewpoint Vista or similar) and HCM platforms (Workday).
• Familiarity with version-controlled, CI/CD-driven data workflows (transformations in source control, automated builds and tests).
• An eye for warehouse cost management — cloud compute and storage bills add up quietly.
• Construction, engineering, or other operations-heavy industry experience.

The person who does well here likes owning the whole thing rather than a corner of it, cares more about whether the numbers are right than whether the pipeline is clever, and would rather ship a model the finance team actually uses than a perfect one nobody asked for. You're comfortable being the person who knows how the data fits together, and you treat "the business owner doesn't quite know what they want yet" as part of the job rather than a blocker.