Senior Data Engineer

Emery Sapp & Sons, Inc. (ESS)•Kansas City, MO

29d•Onsite

About The Position

This is a broad role with real ownership, where the successful candidate will be responsible for owning the entire data platform. The role requires someone confident enough to make decisions independently while also knowing when to ask questions. The position is strictly on-site and requires candidates to be located within driving distance of Columbia, MO or Kansas City, MO, with no remote or hybrid work options available.

Requirements

Strong data engineering fundamentals, including clean, maintainable SQL and Python.
Experience building and operating production data pipelines.
Hands-on experience with a cloud data warehouse (BigQuery preferred; Snowflake, Redshift, or similar acceptable).
Experience with transformation frameworks (dbt strongly preferred) and layered data architecture (raw → staging → modeled).
Solid understanding of dimensional modeling (facts vs. dimensions, star schema design).
Experience with ELT / data replication tools (Fivetran or similar).
Ability to partner with non-technical stakeholders and translate business needs into scalable data solutions.
Comfortable owning decisions and operating independently.

Nice To Haves

Experience with Google Cloud Platform (Cloud Run, Cloud SQL/PostgreSQL, Pub/Sub, Cloud Scheduler, Secret Manager).
Experience integrating ERP systems (Viewpoint Vista or similar) and HCM platforms (Workday).
Familiarity with CI/CD-driven data workflows and version-controlled transformations.
Experience managing warehouse cost optimization.
Background in construction, engineering, or operations-heavy industries.

Responsibilities

Own the data pipelines, including managing automated replication and ingestion into the warehouse.
Monitor performance, troubleshoot failures, and handle schema changes for data pipelines.
Add new data sources as business needs evolve.
Own the platform architecture, including maintaining and evolving the cloud warehouse architecture.
Define layering, naming conventions, performance standards, and cost efficiency for the platform.
Ensure scalability of the platform across multiple subsidiaries.
Own the data modeling, including designing and maintaining dimensional models (facts and dimensions).
Partner directly with Finance, HR, and Operations leaders to translate business needs into data structures.
Build models that support accurate, decision-ready reporting.
Ensure data trust and reliability by building in testing, documentation, and data quality checks.
Maintain clear data lineage and transparency.
Ensure stakeholders trust the accuracy of reporting.