{"id":4110614,"url":"https://alion.io/job/glassdollar-senior-data-engineer-mfd","title":"Senior Data Engineer (m/f/d)","company":{"id":173285,"name":"GlassDollar","domain":"glassdollar.com","url":"https://alion.io/company/glassdollar","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Personio","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Berlin, Germany"],"countries":["DE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Dagster","optional":false},{"name":"dbt","optional":false},{"name":"GDPR","optional":false},{"name":"Great Expectations","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Playwright","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prefect","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"AWS","optional":true},{"name":"Docker","optional":true},{"name":"Embeddings","optional":true},{"name":"GCP","optional":true},{"name":"GraphQL","optional":true}],"status":"live","first_seen_at":"2026-11-11T11:04:18Z","employer_posted_date":"2026-11-11","last_verified_at":"2026-11-11T23:19:43Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"TL;DR\nYou build and run the pipelines behind GlassDollar's company data: ingestion, scraping, enrichment, entity resolution, LLM extraction and signal detection - from first prototype to reliable production. You sit in the product team, report to our Product Owner and work closely with our engineers.Our data has grown faster than our engineering around it. Feeds run, but nothing tells us when they stop. Most relationship records aren't classified yet, duplicates slip through, and some signals users ask for, like headcount changes, have no reliable live source. Making this robust, observable and fast is the job.\nWho uses your work\nPeople in vclOS: Search, lists, projects, PoC tracking, recommendations and weekly company updates.\nOur AI agents & search: Clean fields with provenance, confidence and freshness, measured on our search benchmark.\nAPI and MCP partners: Stable schemas and fields to build on.\nMarketing: Directory, rankings and reports through a quarterly data release.\nWhen these conflict, the product wins.What we're looking for\nWhat you ownIngestion and pipelines: vendor feeds, APIs and scraping jobs - scheduled, idempotent, monitored, with backfills and alerts when a feed stops or drifts.\nSchema and data model: the Postgres schema and data dictionary as the source of truth, including migrations and the Hasura layer on top.\nEntity resolution: matching and deduplicating companies across sources, with measurable precision and recall.\nLLM extraction and classification: pipelines that turn unstructured sources into structured records, evaluated on labelled samples and tuned for cost per record.\nSignals: funding, acquisitions, shutdowns, case studies and headcount as event pipelines with change detection and correct event dates.\nData quality and observability: automated tests and metrics for coverage, accuracy, freshness and completeness.\nData sources: you test vendors, APIs and scraping partners on real samples and give a clear recommendation on quality and cost per datapoint.\nYou join sprint planning and review, triage data errors reported by users and agents, and take datapoints from prototype to production yourself.What you'll do\nYour first 90 daysBy day 90: every feed is monitored and alerting, relationship data is classified with measured precision, entity resolution has a baseline, 3-5 new datapoints are live in production, and our search benchmark has moved against a target you set in month 1.\nMonth 1: baseline and observability\nInventory every pipeline, vendor feed and scraping job; give each one an owner, runbook, monitoring and alerting.\nData dictionary v1 for the top 25 datapoints, with source, lineage and quality numbers.\nAutomated quality checks (coverage, freshness, nulls, duplicates) running on every load.\nQuick win: Scale partnership data point collection.\nMonth 2: corporate-technology relationshipsModel relationship types in the schema: mention, partnership, PoC, implementation, investment, acquisition.\nBuild an LLM classification pipeline for existing and incoming records; report precision on a labelled sample and cost per record.\nSet a recall and lag baseline, then improve the relationship pipeline against it.\nIntegrate a live acquisition source and keep acquired companies out of recommendations.\n Month 3: new datapoints, entity resolution and agentsRun 6-8 time-boxed datapoint experiments, each ending in ship, kill or park. Ship 3-5 to production.\nEntity resolution v2: measurable matching across sources, with a review queue for uncertain cases.\nAdd provenance, confidence and freshness to the top 25 fields, and make customer data (lists, PoCs, notes) usable by agents with proper permissions.\nDocument the data platform: architecture, runbooks and on-call handover.\nRequirements\nWho you areA senior data engineer who ships. You care about reliability and clean data models, and you understand what the data is used for. You'd rather put a rough datapoint live this week than a perfect one next quarter.\nMust-haves\n5+ 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.\nYou've built and run production data pipelines end to end: orchestration (e.g. Airflow, Dagster or Prefect), monitoring, alerting and backfills.\nFluent Python: scrapers and enrichment services (requests or Playwright, pandas or polars), tested and production-ready.\nStrong SQL on a production Postgres database: schema design, migrations, indexing and query performance.\nHands-on with LLMs for extraction and classification, measured on a labelled sample, with an eye on cost and latency.\nPractical entity resolution and deduplication at scale.\nData quality testing (e.g. dbt tests, Great Expectations or your own checks).\nWorking knowledge of GDPR for company and people data.\nBased in Berlin, or willing to relocate.\nNice to have\ndbt, Docker and a major cloud (AWS or GCP); infrastructure as code.\nEvent or signal pipelines: change detection, event dating, alerting.\nVector search or embeddings for matching and retrieval.\nGraphQL / Hasura.\nGerman at reading level: DACH registers are a big part of our upside.\nWorking with data from providers such as PitchBook, Crunchbase, Dealroom or company registers.\nKnowledge of the corporate innovation, VC or emerging technology ecosystem.\nStack: Postgres, Hasura, Python, PostHog and LLM APIs.What this role is not:\nNot BI or analytics: no dashboards for their own sake.\nNot ML research: you use models, you don't train them.\nNot customer workspace data (projects, departments, exports): that stays with product engineering.\nNot a ticket-taker: you decide what's worth building, not just how.\nNot team-building from day one: you lead through scope and technical quality.\nHow we hire\n Intro call (30 min) with our CEO: your background and a data pipeline you built and owned.\n 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.\n Technical review (60 min) with our Product Owner and an engineer: your code, tests, quality checks and trade-offs.\n Team conversations (2 × 30 min) with engineering and customer success, your internal customers.\nOffer.\n What we offerSalary band: €80 - €110k\nReal ownership of a core part of our product and its data platform.\nFast impact: your data shows up for corporate customers, in agent answers and in weekly company updates within weeks.\nA small, senior team. Full-time, Berlin office.","description_format":"text","description_chars":6659,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[{"language":"German","level":"All levels","optional":true}]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":true,"industries":["Angel Networks & Startup Accelerators","B2B Company & Contact Data"],"lifecycle":[{"event":"open","at":"2026-11-11T23:19:43Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":28,"reasons":["conf:1","win:early"],"computed_at":"2026-11-12T00:58:28Z"},"pay":null,"html_url":"https://alion.io/job/glassdollar-senior-data-engineer-mfd","json_url":"https://alion.io/job/glassdollar-senior-data-engineer-mfd.json","meta":{"generated_at":"2026-11-12T00:58:28Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":2260,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-13T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.1,"usd":0.01,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":173285},"rest":"https://alion.io/mcp/rest/get_company?id=173285"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fglassdollar-senior-data-engineer-mfd"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":2,"usd":0.2},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fglassdollar-senior-data-engineer-mfd"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.1,"usd":0.01},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fglassdollar-senior-data-engineer-mfd"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/glassdollar-senior-data-engineer-mfd\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fglassdollar-senior-data-engineer-mfd"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":4110614}},{"tool":"search_jobs","arguments":{"company":"glassdollar.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":4110614},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. Docs: https://alion.io/developers"}}