{"id":122443,"url":"https://alion.io/job/neurons-lab-data-engineer-uaru-language-speaking-2","title":"Data Engineer | Data Scientist — Unstructured Data & AI Pipelines","company":{"id":4957,"name":"Neurons Lab","domain":"neurons-lab.com","url":"https://alion.io/company/neurons-lab","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":87,"open_postings":3,"ghost_share":0,"stale_share":0.333,"repost_share":0,"time_to_fill_p50_days":68,"computed_at":"2026-10-17T05:46:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["HU","PL","RO","ES","BG","CY","CZ","IT","RS","UA","AL","AM","EE","GE","GR","LV","LT","MT","MD","MK","PT","SK"],"hiring_countries_total":22,"salary":null,"salary_estimate":{"min_usd":44000,"max_usd":84000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":20},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"Composio","optional":false},{"name":"GCP","optional":false},{"name":"GDPR","optional":false},{"name":"Google Workspace","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"OpenSearch","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Slack","optional":false},{"name":"SQL","optional":false},{"name":"Unstructured.io","optional":false},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-07-30T13:58:23Z","employer_posted_date":"2026-10-02","last_verified_at":"2026-10-18T00:05:19Z","board_verified":true,"closed_at":null,"days_open":79,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":79},"description":"About the project (description, duration, stage)\nJoin Neurons Lab as a Data Engineer (part-time) on a flagship engagement with a European private investment group - a holding company with a C-level executive team, an investment/portfolio function and an affiliated family office.\nThe programme builds one private, access-scoped context layer over the group's data, then AI skills and agents on top of it. You build the plumbing underneath: Phase 1 (Capture) - turn on ingestion across calls, email, Slack and messengers, board protocols, decks and portfolio updates, live and historical; Phase 2 (Connect) - land it all in the context layer with identity resolved, access scope attached and lineage intact.\nThis is deliberately an unstructured-first data-engineering role. There is no clean warehouse to model: the raw material is transcripts, threads, attachments and years of archive, and the hard problems are entity resolution across people and entities, deduplication, incremental sync, PII handling, and keeping cost sane at volume.\nRoughly eight to ten two-week sprints overall, with your load front-weighted to the first four or five - allocation may flex above 0.5 FTE during Capture and Connect and settle back afterwards.\nStage: active project, start ASAP.\nPart-time, 20 to 30 hours-a-week engagement.\nWhat you'll actually do (example tasks)\nStand up capture by default: notetaker on every call with speaker attribution, plus ingestion from mail, Slack and messengers - designed as opt-out, not opt-in, and reversible if the client changes their mind.\n\nBackfill the archive: years of historical email, Slack, board protocols, decks and portfolio updates - parsed, deduplicated and dated correctly.\n\nBuild document parsing for the awkward long tail: PDFs, scanned board packs, spreadsheets, slide decks, forwarded attachments.\n\nImplement identity / entity resolution: the same person across Slack handle, mail alias and calendar invite; the same portfolio company across a deck, a mail thread and a CRM record.\n\nBuild chunking and embedding pipelines and load the vector + graph stores behind the ontology the architect defines.\n\nImplement incremental sync through the connector layer (MCP / Composio-class) - no full re-crawls, no silent drift, clear handling of edits and deletions.\n\nAttach access scope and provenance to every record at ingestion, so permission-aware retrieval and audit are possible downstream rather than bolted on.\n\nRun PII detection, redaction and retention logic; evidence to the client's security function what is stored, where, and for how long.\n\nOrchestrate with Airflow / Step Functions; build repeatable, monitored pipelines rather than scripts, with alerting when a source stops flowing.\n\nKeep cost and latency under control at volume - batching, incremental embedding, storage tiering - and report the unit economics.\n\nWrite runbooks so the client's own team can operate this after handover.\n\nSkills\nStrong Python and solid SQL\n\nUnstructured-data pipelines: transcripts, mail, chat, documents - parsing, normalisation, deduplication\n\nEmbedding / retrieval infrastructure: chunking strategies, vector stores (pgvector, OpenSearch, Pinecone-class), plus loading a graph store\n\nAPI and connector integration at scale: Google Workspace / M365, Slack, CRM; rate limits, pagination, incremental cursors, webhooks\n\nEntity resolution / record linkage (deterministic + fuzzy) without a clean shared key\n\nOrchestration: Airflow, Step Functions or equivalent; idempotent, restartable jobs\n\nAWS and/or GCP data stack; comfortable in a private / VPC deployment\n\nPII detection, redaction, encryption and retention in practice\n\nClear written English; documents for handover and works well async in a small distributed pod\n\nKnowledge\nGDPR applied to employee-generated data (mail, chat, meeting recordings) and EU data residency across multiple jurisdictions\n\nData lineage, provenance and audit patterns - and why an AI system needs them more, not less\n\nHow retrieval quality depends on ingestion quality - enough understanding of RAG to make the right upstream choices\n\nWell-Architected security and cost practice; awareness of financial-services expectations - a plus\n\nExperience\n4+ years in data engineering, with real unstructured / semi-structured work (not only warehouse modelling)\n\nDemonstrated experience integrating many third-party APIs into one coherent store, including historical backfill\n\nExperience building pipelines feeding an LLM / retrieval system - strong plus\n\nExperience handling sensitive personal data in a regulated or security-sensitive environment\n\nComfortable being the only data engineer on a small (2.5-FTE) pod, at part-time allocation, without 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Republic","iso":"CZ","kind":"country"},{"name":"Italy","iso":"IT","kind":"country"},{"name":"Serbia","iso":"RS","kind":"country"},{"name":"Ukraine","iso":"UA","kind":"country"},{"name":"Albania","iso":"AL","kind":"country"},{"name":"Armenia","iso":"AM","kind":"country"},{"name":"Estonia","iso":"EE","kind":"country"},{"name":"Georgia","iso":"GE","kind":"country"},{"name":"Greece","iso":"GR","kind":"country"},{"name":"Latvia","iso":"LV","kind":"country"},{"name":"Lithuania","iso":"LT","kind":"country"},{"name":"Malta","iso":"MT","kind":"country"},{"name":"Moldova","iso":"MD","kind":"country"},{"name":"North Macedonia","iso":"MK","kind":"country"},{"name":"Portugal","iso":"PT","kind":"country"},{"name":"Slovakia","iso":"SK","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["AI Agents","Financial AI","AI Consulting & 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