Principal Product Manager, Data Platform

Principal Product Manager, Data Platform

Altana is the network for trusted trade, empowering a new era of global commerce by uniting businesses and governments on an AI-powered platform to build a more resilient and secure global supply chain. Enterprises like Boston Scientific, General Atomics, and L.L.Bean use Altana to map and manage their multi-tier value chains. Logistics leaders like UPS and Maersk use Altana to help customers reduce risk and prevent delays. Governments across the U.S. and its allies use Altana to prevent supply chain disruptions, protect national security, combat modern slave labor, and disrupt fentanyl trafficking.

At the heart of our platform is the Altana Knowledge Graph, the world's largest organized body of supply chain data, built from hundreds of billions of trade transactions and continuously enriched by AI and our growing network of enterprise and government customers. Every product we sell runs on the quality of this data and the intelligence we layer on top of it.

We are a team of ambitious, mission-driven engineers, data scientists, and domain experts. If you are passionate about solving complex, real-world problems with global impact, we want you to join us.

The Role: Principal Product Manager, Data Platform

As Principal Technical Product Manager, Data Platform, you will be the owner of Altana's core data asset: the Knowledge Graph, the intelligence derived from it, and the data strategy that determines how it grows. You will define the product vision and roadmap across the graph data model and ontology, the traversal engines that turn graph data into value chains and risk exposures, and the feedback loops that make the graph smarter over time. You will work closely with engineering, data science, AI/ML, and commercial teams to set direction and deliver, and partner with the CDP PM to ensure the two platform roles together cover the full data journey end to end.

We are looking for an experienced technical product leader who is passionate about data quality, coverage, and the hard infrastructure problems that most people would rather work around than solve. You have a track record of turning ambiguous data challenges into well-scoped product investments, and of working closely with engineering to ship systems that compound in value over time.

Your mission is to make Altana's graph the most accurate, complete, and trustworthy picture of global supply chains in existence. Today, the graph captures the world in terms of facilities and trade transactions. That foundation is strong. But the insight customers need, tracing a specific product upstream through multiple tiers, understanding where risk lives within it, and trusting what they see enough to act on it in a compliance filing or board presentation, requires more: a richer data model, smarter traversal, continuously expanding data coverage, and a quality program that measures and improves the graph with every release.

You will own the full intelligence stack that sits between raw graph data and the customer experience: from the data model and publication infrastructure, through the value chain and risk traversal engines, to the data strategy and feedback loops that keep the graph ahead of what customers need next.

This is an AI-forward role. AI is not a feature layer here, it is central to how the graph gets built, enriched, and improved. You will define where machine learning, large language models, and agentic workflows replace what previously required human curation or manual pipelines, and you will set the product direction for how AI-driven enrichment compounds over time into a data asset no one else can replicate.

What You'll Do

1. Knowledge Graph: Data Model, Ontology & Publications

• Own the product vision for Altana's Knowledge Graph, the facility- and transaction-based foundation of everything we build, including the data model, entity and relationship schema, and the ontology that governs how facilities, companies, products, and trade flows are represented, resolved, and versioned.
• Define the graph publication strategy: how and when the graph is refreshed, how downstream consumers are protected from breaking changes, and how the data model evolves to serve enterprise and government use cases without fragmenting into incompatible variants.
• Govern the modeling tradeoffs that determine graph quality for enterprise users, company consolidation for supply chain connectivity, facility granularity for upstream tracing, commodity classification depth, and own the roadmap for AI-driven enrichment that improves coverage and accuracy over time.

2. Value Chain & Risk Traversal

• Own the product design and roadmap for the traversal engines that sit between the graph and the Customer Data Platform: the value chain engine that walks supplier-product relationships upstream tier by tier, and the risk traversal engine that propagates exposure signals (forced labor, export controls, tariff liability, critical mineral flags) across those same relationships.
• Define the parameters, filtering logic, and output contracts that these engines expose to downstream consumers, ensuring that new compliance questions can be answered through configuration rather than a new custom pipeline each time.
• Partner with the CDP PM to ensure the traversal layer produces output that is accurate, interpretable, and built to the quality bar that enterprise customers need for compliance filings, product passports, and regulatory submissions.

3. Data Strategy, Expansion & Customer Feedback Loops

• Own Altana's data strategy: continuously assessing where the Knowledge Graph has gaps, anticipating which new data sources, third-party vendors, government datasets, logistics networks, industry consortia, would most meaningfully expand coverage and connectivity for the customers and use cases that matter most, and making the case for where we invest.
• Define how customer interactions with the platform feed back into improving the graph: when customers confirm, correct, or contribute supply chain data, those signals should systematically improve the underlying data and models, making the graph smarter through use rather than only through periodic data acquisition.

4. Graph & Value Chain Quality

• Define and own Altana's graph and value chain quality framework, the dimensions that matter across use cases (entity resolution accuracy, coverage and recall of real supply chain relationships, product classification precision, data freshness, depth of tier penetration), and build the measurement infrastructure and baselines to track improvement systematically over time.
• Own quality gating before graph data and traversal output reaches downstream consumers, and design the confidence signals that allow customers to distinguish verified from probabilistic data rather than treating all output as equally reliable.
• Work with AI/ML to productize AI-assisted approaches to entity resolution and graph enrichment for hard and ambiguous cases, and build the feedback loop that turns gaps in traversal output into specific, measurable improvement priorities for the graph and data science teams.

Who You Are

• Deep Technical Platform Expertise: You have 810+ years of product management experience with a strong focus on data platforms, graph infrastructure, or complex data pipelines. You engage comfortably with engineers and data scientists on schema design, pipeline architecture, and data modeling tradeoffs, not just at a conceptual level, but at the level of actual decisions.
• Graph & Ontology Fluency: You understand how graph data works, why modeling products as first-class entities changes what you can query, and what tradeoffs come with different resolution strategies. You've worked with large-scale data systems and know what it means to balance precision with connectivity when the data has to serve very different audiences.
• Pipeline & AI Thinker: You understand data ingestion, transformation, and orchestration, and you are genuinely excited about where AI changes what's possible, automated matching, AI-assisted classification, agentic workflows for data enrichment and validation. You know how to translate an AI capability into a product surface that actually works for customers.
• End-to-End Systems Thinker: You can hold the full data journey in your head: from a customer's raw file through ingestion, entity matching, graph traversal, filtering, and enrichment, all the way to the supply chain view a compliance officer is acting on. You design for the whole system, trace quality failures to their root causes, and anticipate where things break under real-world conditions.
• Quality-Obsessed: You are not satisfied with "we're always improving." You define what good looks like, measure where you are, and build the infrastructure to track improvement systematically over time. You have built data quality frameworks and understand how to create feedback loops that make a platform smarter with use.
• Enterprise and Government Fluency: You can earn trust with a CTO at a Fortune 500 by speaking fluently about data governance, integration architecture, and security. You can also hold a credible conversation with a trade compliance officer about product classification, BOM reconciliation, and customs data. You have equal empathy for the analyst who needs to know whether what they're looking at is actually right.
• Builder and Communicator: