Your Mission

Is to help SPARETECH bring order to one of the most fragmented data problems in industrial operations: spare parts data. You will contribute to a platform that ingests, enriches, and normalises millions of parts records from manufacturers, suppliers, and customers worldwide, turning inconsistent, unstructured inputs into a trusted catalog that MRO teams rely on every day.

In this role, you will take end-to-end ownership of the data platform lifecycle: defining and improving the methodologies that grow the catalog, building and operating the pipelines that onboard partner data, strengthening the quality and governance layer that makes the data trustworthy, and delivering the tooling that continuously increases the value of SPARETECH’s data as a product. You will sit at the intersection of engineering, data management, and customer-facing product, and be accountable for the platform’s performance and impact.

We offer you the opportunity to

• Own the end-to-end roadmap: Drive data ingestion, enrichment, normalisation, and quality across partner-supplied data sources such as manufacturers, suppliers, resellers, and other data providers.
• Drive data expansion strategy: Identify, evaluate, and prioritize new data acquisition opportunities, balancing coverage, quality, and business impact.
• Build data quality and governance at scale: Define what “good” looks like for a parts record, set quality thresholds, and build systems to monitor and improve data quality continuously.
• Improve discoverability and trust: Shape the catalog experience so internal teams and enterprise customers can search, access, analyze, and trust the data.
• Establish instrumentation and observability: Define the metrics that matter, set decision thresholds, and make health signals visible so teams can act on data quality signals instead of escalating by intuition.
• Enable AI-powered features: Ensure the data layer is clean, structured, and AI-ready to support intelligent parts matching and recommendation capabilities.
• Be accountable for shipped outcomes: Validate and test data pipelines, quality checks, and customer-visible behavior before release.

Become a part of our team if you

• Bring product experience: 2-4 years of product management experience, ideally with exposure to data and analytics products.
• Work hands-on with data: Strong SQL fluency and experience using production data to define requirements, validate quality, and challenge product intuition.
• Turn ambiguity into clarity: Proven ability to translate messy, ambiguous data problems into clear requirements and measurable outcomes.
• Communicate across audiences: Comfortable going deep with software engineers and data analysts, and explaining the same problem to leadership and customers.
• Work AI-first: You are comfortable using AI tools on a daily basis to improve efficiency, iterate faster, and adapt quickly as you learn.
• Nice-to-haves: Experience with industrial, supply chain, or B2B product data; product catalog ingestions; catalog standards such as ETIM, ECLASS, or UNSPSC; and BI tools like Metabase.

You can expect

Our base salary is just one component of the competitive total rewards strategy. We value the well-being and professional growth of our team. That's why we offer:

• Paid Time Off: 20 days of vacation, plus public holidays.
• Hybrid Work Model: Enjoy a flexible work setup, typically spending two days per week in our Chicago office.
• Professional Development: An annual training budget of $1,000 to support your growth in a dynamic, startup environment.