Forward Deployed Engineer (FDE)

Seaboard Energy•Merriam, KS

3d•Onsite

About The Position

We have an exciting opportunity for a Forward Deployed Engineer (FDE) supporting Seaboard Energy, based out of our Merriam, KS office. This role is build-first and embedded directly with the Energy business. It combines hands-on data work (gathering data, building and consuming APIs, and delivering on a modern data platform) with the innovation work of finding better ways to operate the business through a business ontology and the applications, agents, and tools built on top of it. Roughly 70 to 80% of the role is hands-on build using a data platform and AI-Assisted Development, 10% is orchestrating delivery (running standups, coordinating across the business and IT teams, clarifying priority, and confirming direction with leadership), and 10 to 20% is setting and operationalizing the technical patterns and standards the team follows. The FDE leads through the work, shipping the hardest, highest-leverage parts of an initiative and turning what they build into reusable patterns for the rest of the team.

Requirements

4+ years of technical delivery experience.
Track record of shipping production software and data pipelines hands-on, including within the past 12 months. This role is build-first.
Hands-on experience building data solutions on Microsoft Fabric or an equivalent modern cloud data platform.
Experience consuming APIs, including debugging integration failures.
Experience with data warehouse or lakehouse design patterns: medallion architecture, data modeling, and incremental load strategies.
Hands-on proficiency with AI-Assisted Development.
Familiarity with cloud platforms (Azure, AWS) and modern DevOps and CI/CD practices.
Demonstrated ability to set technical patterns and standards that other engineers adopt.
Demonstrated ability to lead cross-team initiatives, manage stakeholders, and operate in ambiguity to drive outcomes.
Ability to break large problems into small deliverable increments, maintaining momentum and transparency through frequent releases.
Understanding of business operations and the ability to connect data and architecture decisions to business outcomes.

Nice To Haves

Knowledge of Foundry and AIP (ontology, AIP Logic, Workshop, Pipeline Builder), or equivalent enterprise data and ontology platform experience with a clear ability to ramp quickly.
Background in the liquid fuels or oil and gas industry.
Background in manufacturing, logistics, or commodities.
Experience with Microsoft Fabric workloads: Lakehouse, Data Warehouse, Dataflows Gen2, Notebooks, Pipelines, and/or Real-time Intelligence.
Experience with PySpark, SQL, and Python for data engineering work at production scale.
Experience building and maintaining semantic models.
Experience with Azure API Management, Azure Functions, or other Azure integration services.
Degree in Computer Science, Information Systems, Data Engineering, or a related field.

Responsibilities

Own end-to-end delivery of the most complex parts of Energy's data and AI initiatives from scoping and design through build, test, and rollout.
AI-Assisted Development is the primary delivery mode, with pro-code used where it is not enough.
Gather, ingest, transform, and serve data across the Energy business — building the pipelines, lakehouses, semantic models, and OneLake medallion structures in the data platform (Dataflows Gen2, Notebooks in PySpark and SQL, and Fabric Pipelines) that make data reliably available for reporting, AI features, and downstream applications.
Build or consume, and maintain APIs — designing clear contracts, implementing endpoints, and integrating with internal systems and external vendors so data flows where it is needed.
Model and extend the business ontology in Foundry and AIP. This covers the objects, links, actions, and write-back paths that power agents, AIP Logic workflows, and internally built applications.
Build the applications, agents, and tools on top of the ontology and data platform that give the Energy business better, more innovative ways to operate.
Embed within Energy teams to acquire deep domain knowledge, iterate in tight feedback loops, and ship working software that drives measurable business outcomes.
Confirm releases and data assets are operationally ready, with monitoring, runbooks, deployment approach, and escalation paths in place before go-live.
Stay current on data platforms, emerging AI tools, agent and ontology platforms, and modern delivery practices, and pull proven patterns into the work as quickly as they earn their place.
Run (or participate in) the day-to-day cadence for the initiative, including standups, working sessions, and lightweight planning (milestones, dependencies, cutovers) that keeps work moving and dependencies visible.
Coordinate across the Energy business and IT teams (Architecture, Platform Engineering, Data Platform, and external vendors) to surface and resolve blockers before they slow delivery.
Clarify priority when the queue is ambiguous: pull the right people into the room, force the trade-off conversation, and confirm direction with the sponsoring leader so the team can keep building.
Set reusable patterns and standards across AI-Assisted Development, data pipeline and semantic-model design, API integration, and ontology modeling so the team converges on what works.
Run design and implementation reviews to raise quality and reduce long-term support burden.
Mentor others through pairing, code review, and quick coaching moments embedded in the work.
Document the rationale for tool and pattern choices so others can follow without rediscovering the path, and retire patterns that no longer work.