Lead Data Engineer | Careers | Knowledge Gate Group

Engineering|Copenhagen, Denmark (Hybrid)|Full-time

Why we're hiring a Lead Data Engineer

We're building the expert intelligence layer for scientific research: a knowledge graph that connects the world to leading experts based on publications & clinical trials in precise ontologies. You'll design pipelines that ingest millions of life-science records, shaping a graph of how scientific knowledge is modelled, enriched, & served.

This is true green-fields work. Your decisions will lay the data foundations for our entire expert intelligence platform.

What You'll Do

You will be working at the intersection of science, data engineering & AI to build expert intelligence.

Own data end-to-end, design & run data pipelines turning millions of scientific records into a knowledge graph.

Implement precision entity resolution & enrichment, disambiguate & enrich experts from noisy data sources.

Utilise LLM workflows where it makes sense, for entity extraction, relationship inference & quality validation

Develop vector embeddings & semantic search capabilities to power expert discovery & similarity matching.

Model life-science entities & relationships, ontologies, author networks, publication & clinical trial metadata.

Build graph & vector data access, performant, accessible, reliable, observable & testable data access.

Move fast & ship value incrementally, done-and-iterating beats perfect-and-pending.

Radiate intent & document your thinking openly, collaborating async-first in a hybrid environment

Lead when you're the expert, follow when someone else is, challenging assumptions when necessary

Use AI as a daily force multiplier across coding, schema design, debugging, optimisation & validation.

Destroy your colleagues at Geoguessr (optional but strongly encouraged).

What You'll Need

Technical Skills

Graph Databases : Neo4j, ArangoDB, Neptune; schema design, relationship modelling, query optimisation.

Python Data Engineering : ETL development; pandas/polars; distributed processing with Spark or Dask.

Entity Resolution : Deduplication, merging, enrichment across heterogeneous scientific data sources.

AI-Assisted Data Extraction : LLM entity extraction, schema generation & quality validation.

Vector Search : Experience with Pinecone, FAISS, Qdrant, or Weaviate; embeddings, hybrid retrieval.

Workflow Orchestration : Robust, observable pipelines using Airflow or Dagster.

Data Formats & Standards : Parquet, JSONL, RDF/Turtle; selecting formats for graph & semantic use cases.

Embedding Models : Understanding of HuggingFace/OpenAI models, dimensionality tradeoffs & cost.

Executive Skills

Ownership mindset : Treat data & schemas as products powering multiple domains.

Strategic evaluation : Choose tech aligned with our scale, latency expectations, & roadmap needs.

Process engineering : Build reliable, repeatable & maintainable workflows.

Cross-functional communication : Bridge product engineers & scientific domain teams.

Comfort with scientific data realities : Deep rabbit holes of sprawling complexity.

Strong Bonus

Life Sciences familiarity : Publication, clinical trial, institutional, ontologies (MeSH, SNOMED, Gene Ontology).

Hands-on with scientific datasets : OpenAlex, PubMed/MEDLINE, ORCID, Semantic Scholar, ClinicalTrials.gov

Why You Might Hate It Here

You want predictability & routine.

You dislike documenting or sharing your thinking openly.

You see AI as a threat rather than an amplifier.

You're looking for a "safe" corporate environment - we're not that.

We mean this sincerely: if those points do not work, you'll be happier elsewhere.

Why You'll Love Working Here

Real Autonomy: You'll own outcomes, not tickets. This is your domain - you'll define data strategy.

Greenfield Opportunity : Build the from scratch. Your decisions shape our data capabilities for years.

Mission That Matters : Your work directly enables research - accelerating scientific breakthroughs.

AI-First Culture : We use AI as a creative & operational partner across every function.

High Impact : Every domain depends on what you build. Expert coverage directly drives our success.

Success Metrics (6-month target)

Expert Coverage : Knowledge graph spans 1+ million experts with rich profile data & relationships.

AI & Platform Enablement : AI & other domains consuming knowledge graph insights.

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