[Hiring] Data Engineer @Neurons Lab

Role Description

Join 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. The 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.

This 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. Roughly 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. Stage: pre-contract / design-partner negotiation.

Reporting: the AI Architect on the engagement, working alongside an AI Analyst; the client's Head of Security is in the working group from day one. Part-time, 20-hour-a-week engagement.

Qualifications

• Strong Python and solid SQL
• Unstructured-data pipelines: transcripts, mail, chat, documents — parsing, normalisation, deduplication
• Embedding/retrieval infrastructure: chunking strategies, vector stores (pgvector, OpenSearch, Pinecone-class), plus loading a graph store
• API and connector integration at scale: Google Workspace / M365, Slack, CRM; rate limits, pagination, incremental cursors, webhooks
• Entity resolution / record linkage (deterministic + fuzzy) without a clean shared key
• Orchestration: Airflow, Step Functions or equivalent; idempotent, restartable jobs
• AWS and/or GCP data stack; comfortable in a private / VPC deployment
• PII detection, redaction, encryption and retention in practice
• Clear written English; documents for handover and works well async in a small distributed pod

Requirements

• 4+ years in data engineering, with real unstructured/semi-structured work (not only warehouse modelling)
• Demonstrated experience integrating many third-party APIs into one coherent store, including historical backfill
• Experience building pipelines feeding an LLM/retrieval system — strong plus
• Experience handling sensitive personal data in a regulated or security-sensitive environment
• Comfortable being the only data engineer on a small (2.5-FTE) pod, at part-time allocation, without hand-holding

Benefits

• Part-time engagement with flexible hours
• Opportunity to work on a high-impact project with a European private investment group
• Collaboration with experienced professionals in AI and data engineering