Senior Data Engineer

The posting, as Fusion Worldwide wrote it

Role Summary

Fusion Worldwide is a global open market distributor of electronic components. When a supply chain breaks because of an allocation, a shortage, or a part going end-of-life, we're who the world's largest manufacturers call.

That business runs on knowing things: which companies are real and active, which parts substitute for which, who is likely to need what. Our traders make those calls in hours, using what the platform shows them.

We run a production data and intelligence platform. RMS is our system of record, a custom internal ERP we build and maintain. Every order, quote, and transaction lives there. Our platform sits on top. It reads from RMS, enriches and models that data, and writes operational results back.

Tech Stack

• Data: Microsoft SQL Server, Azure SQL
• Languages: Python, PySpark, SQL, TypeScript
• AI: Claude connected directly to our platform for agentic development
• Applications: Custom applications in React, APIs consumed by Web RMS
• Integration: RMS, HubSpot, vendor APIs
• Cloud: Microsoft Azure
• Work management: Atlassian (Jira, Confluence)

Platform & Data

• Work with the object model covering companies, parts, offers, and demand signals. That includes adding object types, properties, and relationships as the data needs them.
• Build and maintain ingestion from RMS, Azure SQL, HubSpot, and vendor APIs, along with the transforms, pipelines, and automations behind it.
• Build and maintain the quality gates, including data expectations that fail the build, freshness checks against declared SLAs, and tests that catch problems nobody would otherwise notice.
• Tune performance across query plans, index design, materialization strategy, and caching.
• Help with incident response when pipelines or write-backs break, which may include on-call.

System of Record Write-backs

• Work on the write-back path into RMS. Scores, enrichment, resolved entities, and operational flags get written back to the system of record.
• Handle idempotency, conflict handling, and reconciling the two sides when they disagree.

APIs, Applications & AI

• Design the APIs that UIs in Web RMS use to pull platform data. You work on the contracts and the versioning, and you make sure the APIs are fast enough for a UI.
• Build custom applications.
• Build agent workflows that read the object model and act on it, such as triaging inbound RFQs, picking out pricing signals, and flagging anomalies in offers.
• Build and maintain the agent setup the team develops with, including instructions, tool connections, and the checks on agent output.

How We Work

Our team built this platform with AI agents, and you'd keep working that way. We're rolling out a federated team model, and this role sits on the core data platform team. Claude connects directly to our platform and writes transforms, queries our data, runs audits, and reads the object model as it goes.

You'll have a budget for AI tooling and compute, and we won't make you fight
[…]
AI agents on production systems, with specifics on where they help and where they quietly fail.

• Building systems other people can audit. Lineage that holds up, parameters stored as versioned data, and outputs a non-engineer can challenge.
• Clear written communication. We write a lot of documents, and this role writes many of them.

Strongly preferred

• 3+ years hands-on experience with a production data platform, including object modeling, building and shipping pipelines, and shipping an application that people use
• Python and PySpark, including catching what an agent gets wrong, such as a transform that looks right but skews the join, a window function that silently drops rows, or a fix that passes tests and breaks the contract downstream
• Dimensional modeling and schema design judgment
• Entity resolution, master data, taxonomies, or knowledge graphs
• React
• Process mining
• Jira, including connecting to it with Claude or other AI tools
• ERP integration experience • Streaming and event-driven ingestion (Kafka, CDC)
• Electronics distribution, supply chain, or industrial B2B data

Explicitly not required

• A PhD
• Deep learning research or model training. We use frontier models; we don't train them
• Prior distribution-industry experience
• Front-end as a primary skill. The job is data and backend

Ramp

• 30 days: You're shipping transforms and object-model changes to production through our existing promotion path, and you've found at least one thing we got wrong.
• 90 days: You're working across the backend, including the RMS write-back path, and an application you built is in daily use on the trading floor.

Application Question: Instead of a cover letter, we'd rather have your answer to one question: Describe something you built on a data platform that you'd model differently if you started again today, and what changed your mind. Our interviews include a hands-on build session. You'll use Claude, and you'll explain every line you ship.