Staff Data Engineer (Entity Resolution & Identity)
Staff-level data engineer at Zeno, a legal AI startup in Rotterdam, building core entity resolution and identity systems — matching, merging, and versioning legal entities across large volumes of unstructured legal data. Core tech focus: Python, record linkage, identity graphs, and explainable, reversible merge logic.
- Compensation
- Not disclosed
- Location
- Netherlands
- Work setup
- Not specified
- Seniority
- Staff
- Employment
- Contract
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- Staff-level experience in data engineering, solving non-trivial identity, deduplication, or consistency problems
- Strong system design instincts and ownership mindset
- Experience building complex, long-lived production systems
- Strong programming skills (e.g., Python or similar)
- Hands-on work with unstructured or semi-structured data
Nice to have
- Experience in legal, government, or other high-complexity document domains
- Experience building human-in-the-loop review systems
Full description
The intelligence engine for legal work, providing AI-powered legal research, contract review, risk assessment, and drafting tools for in-house legal teams.
Staff Data Engineer (Entity Resolution & Identity)
As a Staff Data Engineer — Entity Resolution & Identity at Zeno, you will build and own core systems for entity resolution and identity management in a legal AI platform, handling unstructured legal data across jurisdictions. This senior role involves designing durable, explainable systems for matching, merging, and versioning entities with a focus on precision, performance, and maintainability.
Job Description
Staff Data Engineer — Entity Resolution & Identity
About the role
As a Staff Data Engineer — Entity Resolution & Identity, you will build and own the core systems that power Zeno’s data and AI platform. Your work sits at the heart of the product: determining what is the same, what is different, what is a version, and how those decisions evolve over time.
This role is centered on hard engineering problems. You’ll work with large volumes of unstructured and semi-structured legal data from many sources, formats, jurisdictions, and time periods. You’ll design systems that can evolve without constant reprocessing, where every decision is explainable, reversible, and traceable.
You operate at senior-to-staff level and take ownership of long-lived, mission-critical infrastructure where correctness, performance, and maintainability matter deeply.
What you’re working on
What you’ll build
- A durable entity resolution framework
- Canonical entities with stable IDs that survive logic and data changes
- Identity graphs with merge/split semantics and full provenance
- Matching and consensus logic balancing precision, recall, and durability
- Incremental recomputation (no “reprocess the world” when logic improves)
- System-level data quality, validation, and observability
You’ll build systems that can reliably answer:
- Are these two records the same real-world legal entity?
- Is this a duplicate, a variant, a new version, or something else?
- How do we evolve matching logic without rebuilding everything?
- How do we make merges reversible and decisions explainable?
Constraints you’ll work under:
- Unstructured and semi-structured legal data
- Conflicting, incomplete, and shifting sources of truth