Data Platform Engineer-Solutions Delivery

YOUR OPPORTUNITY

We have an exciting opportunity for a Data Platform Engineer at our Merriam, KS office. The Data Platform Engineer is the core builder on the Data Platform team within Solutions Delivery, focused on designing, building, and operationalizing data solutions, primarily on Microsoft Fabric. This role is build-first: up to 80% of time is hands-on build — designing and shipping data pipelines, lakehouses, semantic models, APIs, and integrations that power Seaboard's AI and analytics capabilities.

The remaining 20% is split between orchestrating delivery (10%) and defining the technical standards and patterns that govern the team's platform work (10%). This role sits at the intersection of data engineering, API development, and platform operations. The right candidate is an engineer who brings deep Microsoft Fabric expertise, writes clean and maintainable code, and understands that reliable data pipelines and well-designed APIs are as much a product as any application.

 

ABOUT US

At Seaboard Foods, we create the most sought-after pork. As one of the nation’s leading pork producers and exporters, we are committed to producing high-quality food responsibly while connecting every step from our farms to family tables. More than 5,400 employees across five states support our farms, feed mills, and processing operations, helping deliver Prairie Fresh® pork to customers in more than 30 countries worldwide. At Seaboard Foods, we value teamwork, integrity, safety, and continuous improvement. As a Fortune 500 employer and recent Business Journal “Best Places to Work” nominee, we are proud to foster a culture where employees can grow, contribute, and make a meaningful impact.

 

ESSENTIAL DUTIES AND RESPONSIBILITIES

This list is not intended to be all-inclusive, and other duties may be assigned.

The work breaks down into three buckets:

Hands-on build (up to 80% of time)

• Design, build, test, and deliver data pipelines, data transformations, lakehouses, data warehouses, and semantic models in Microsoft Fabric using Dataflows Gen2, Notebooks (PySpark, SQL), and Fabric Pipelines.
• Ingest, transform, and serve data across the business — building the OneLake structures and medallion architecture patterns that make data reliably available for reporting, AI features, and downstream applications.
• Build, consume, and maintain APIs as appropriate — designing clear contracts, implementing endpoints, and integrating with internal systems and external vendors to ensure data flows where it is needed.
• Develop and maintain semantic models, ensuring data accuracy, performance, and reusability for reporting and AI-driven use cases across the business.
• Partner with Forward Deployed Engineers (FDEs) and business stakeholders to understand data requirements and iterate rapidly in tight feedback loops.
• Implement monitoring, alerting, and operational runbooks for platform components; partner with Application Support to confirm data assets and integrations are operationally ready before go-live.
• Stay current on Microsoft Fabric capabilities — new workloads, performance features, governance tooling — and pull proven patterns into the team’s work as they mature.

Orchestrating delivery (10% of time)

• Participate in team standups, planning sessions, and delivery ceremonies; surface data dependencies, blockers, and capacity constraints proactively so they can be resolved before they slow delivery.
• Coordinate with FDEs, Architecture, and external partners to align on data contracts, API specifications, and integration patterns before build begins, reducing rework and integration friction.
• Clarify data requirements and priority when the scope is ambiguous: pull the right stakeholders into the conversation, confirm direction, and keep build work moving.

Standards and patterns (10% of time)

• Define and document reusable patterns for data pipeline design, semantic model development, and API integration in the Seaboard context, so the team converges on what works rather than rediscovering it on each initiative.
• Conduct data and API design reviews to raise quality, catch performance or consistency issues early, and reduce long-term maintenance burden.
• Maintain documentation for data assets, schemas, and integration cont