Senior Data Engineer (m/f/d).

TL;DR

• People in vclOS: Search, lists, projects, PoC tracking, recommendations and weekly company updates.
• Our AI agents & search: Clean fields with provenance, confidence and freshness, measured on our search benchmark.
• API and MCP partners: Stable schemas and fields to build on.
• Marketing: Directory, rankings and reports through a quarterly data release.

What we're looking for

• Ingestion and pipelines: vendor feeds, APIs and scraping jobs – scheduled, idempotent, monitored, with backfills and alerts when a feed stops or drifts.
• Schema and data model: the Postgres schema and data dictionary as the source of truth, including migrations and the Hasura layer on top.
• Entity resolution: matching and deduplicating companies across sources, with measurable precision and recall.
• LLM extraction and classification: pipelines that turn unstructured sources into structured records, evaluated on labelled samples and tuned for cost per record.
• Signals: funding, acquisitions, shutdowns, case studies and headcount as event pipelines with change detection and correct event dates.
• Data quality and observability: automated tests and metrics for coverage, accuracy, freshness and completeness.
• Data sources: you test vendors, APIs and scraping partners on real samples and give a clear recommendation on quality and cost per datapoint.

What you'll do

• Inventory every pipeline, vendor feed and scraping job; give each one an owner, runbook, monitoring and alerting.
• Data dictionary v1 for the top 25 datapoints, with source, lineage and quality numbers.
• Automated quality checks (coverage, freshness, nulls, duplicates) running on every load.
• Quick win: Scale partnership data point collection.

• Model relationship types in the schema: mention, partnership, PoC, implementation, investment, acquisition.
• Build an LLM classification pipeline for existing and incoming records; report precision on a labelled sample and cost per record.
• Set a recall and lag baseline, then improve the relationship pipeline against it.
• Integrate a live acquisition source and keep acquired companies out of recommendations.

• Run 6–8 time-boxed datapoint experiments, each ending in ship, kill or park. Ship 3–5 to production.
• Entity resolution v2: measurable matching across sources, with a review queue for uncertain cases.
• Add provenance, confidence and freshness to the top 25 fields, and make customer data (lists, PoCs, notes) usable by agents with proper permissions.
• Document the data platform: architecture, runbooks and on-call handover.

Requirements

• 5+ years in data engineering (or backend engineering with a data focus), ideally with company, market or B2B data at a data business, marketplace or SaaS company.
• You've built and run production data pipelines end to end: orchestration (e.g. Airflow, Dagster or Prefect), monitoring, alerting and backfills.
• Fluent Python: scrapers and enrichment services (requests or Playwright, pandas or polars), tested and production-ready.
• Strong SQL on a production Postgres database: schema design, migrations, indexing and query performance.
• Hands-on with LLMs for extraction and classification, measured on a labelled sample, with an eye on cost and latency.
• Practical entity resolution and deduplication at scale.
• Data quality testing (e.g. dbt tests, Great Expectations or your own checks).
• Working knowledge of GDPR for company and people data.
• Based in Berlin, or willing to relocate.

• dbt, Docker and a major cloud (AWS or GCP); infrastructure as code.
• Event or signal pipelines: change detection, event dating, alerting.
• Vector search or embeddings for matching and retrieval.
• GraphQL / Hasura.
• German at reading level: DACH registers are a big part of our upside.
• Working with data from providers such as PitchBook, Crunchbase, Dealroom or company registers.
• Knowledge of the corporate innovation, VC or emerging technology ecosystem.

• Not BI or analytics: no dashboards for their own sake.
• Not ML research: you use models, you don't train them.
• Not customer workspace data (projects, departments, exports): that stays with product engineering.
• Not a ticket-taker: you decide what's worth building, not just how.
• Not team-building from day one: you lead through scope and technical quality.

How we hire

• Intro call (30 min) with our CEO: your background and a data pipeline you built and owned.
• Paid case (about one day). Around 500 companies from our database with their corporate relationship records. Build a small pipeline that classifies the relationships and deduplicates the companies; report precision on a sample, cost per record, and how you'd run it in production.
• Technical review (60 min) with our Product Owner and an engineer: your code, tests, quality checks and trade-offs.
• Team conversations (2 × 30 min) with engineering and customer success, your internal customers.
• Offer.

• Salary band: €80 – €110k
• Real ownership of a core part of our product and its data platform.
• Fast impact: your data shows up for corporate customers, in agent answers and in weekly company updates within weeks.
• A small, senior team. Full-time, Berlin office.