Senior Data Engineer (AI)

Location: United States

Company

hirify.global is a global open-market distributor of electronic components operating a production data and intelligence platform for supply-chain and trading workflows.

What you will do

• Build and maintain ingestion, transformation, automation, and quality pipelines across RMS, Azure SQL, HubSpot, and vendor APIs.
• Extend the object model for companies, parts, offers, and demand signals, including new object types, properties, and relationships.
• Develop write-back flows into RMS for scores, enrichment, resolved entities, and operational flags while handling transactional integrity, idempotency, conflicts, and reconciliation.
• Design versioned APIs for Web RMS applications and build custom applications and agent workflows for RFQ triage, pricing signals, and offer anomaly detection.
• Improve query performance through execution-plan analysis, index design, materialization, caching, and database modeling.
• Support incident response, maintain agent instructions and tool connections, and review and correct AI-generated code and transformations.

Requirements

• 8+ years of software engineering experience focused on backend and data engineering.
• Production data-platform experience with technologies such as Databricks, Snowflake, Spark, or dbt.
• Expert SQL and deep relational-database experience, especially with SQL Server, including execution plans, indexing, and schema design.
• Experience with system-of-record write-backs, API versioning, caching, performance engineering, Git, code review, CI/CD, data governance, security, and audit requirements.
• Production experience building and operating LLM systems and AI agents, including evaluation, guardrails, cost, latency, failure analysis, and explainable outputs.
• Strong written communication and experience delivering features end to end from scoping and POC through iteration and release.

Nice to have

• 3+ years of hands-on production data-platform experience, including object modeling, pipeline delivery, and applications used by business teams.
• Python, PySpark, dimensional modeling, schema design, entity resolution, master data, taxonomies, or knowledge graphs.
• React, process mining, Jira integrations with AI tools, ERP integration, streaming or event-driven ingestion, Kafka, or CDC.
• Experience with electronics distribution, supply chain, or industrial B2B data.

Culture & Benefits

• Work on a core data-platform team using Claude directly against the platform for transforms, queries, audits, and object-model work.
• Receive a budget for AI tooling and compute with direct access to model resources.
• Follow written engineering standards, runbooks, approved project plans, and documented lessons from past failures.
• Take ownership of product work, including problem scoping, POCs, prioritization, trader feedback, and release decisions.
• Agents generate much of the code, while engineers maintain the agents, validate their output, and resolve production failures.

Hiring process

• Answer a question about a data-platform system you would model differently today and what changed your mind.
• Complete a hands-on build session using Claude and explain every line shipped.
• Initial ramp targets include shipping production transforms within 30 days and having an application in daily trading-floor use within 90 days.