**About the project** ***(description, duration, stage)*** ---------------------------------------------------------- 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.** **What you'll actually do** ***(example tasks)*** ------------------------------------------------- * Stand 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. * **Backfill the archive**: years of historical email, Slack, board protocols, decks and portfolio updates — parsed, deduplicated and dated correctly. * Build **document parsing** for the awkward long tail: PDFs, scanned board packs, spreadsheets, slide decks, forwarded attachments. * Implement **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. * Build **chunking and embedding pipelines** and load the vector \+ graph stores behind the ontology the architect defines. * Implement **incremental sync** through the connector layer (MCP / Composio\-class) — no full re\-crawls, no silent drift, clear handling of edits and deletions. * Attach **access scope and provenance to every record at ingestion**, so permission\-aware retrieval and audit are possible downstream rather than bolted on. * Run **PII detection, redaction and retention** logic; evidence to the client's security function what is stored, where, and for how long. * Orchestrate with **Airflow / Step Functions**; build **repeatable, monitored pipelines rather than scripts**, with alerting when a source stops flowing. * Keep **cost and latency under control** at volume — batching, incremental embedding, storage tiering — and report the unit economics. * Write **runbooks** so the client's own team can operate this after handover. **Skills** ---------- * 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 **Knowledge** ------------- * **GDPR** applied to employee\-generated data (mail, chat, meeting recordings) and EU data residency across multiple jurisdictions * Data **lineage, provenance and audit** patterns — and why an AI system needs them more, not less * How **retrieval quality depends on ingestion quality** — enough understanding of RAG to make the right upstream choices * **Well\-Architected** security and cost practice; awareness of financial\-services expectations — a plus **Experience** -------------- * **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
David Muñoz
Indeed · HR
David Muñoz
Indeed · HR
