Data Engineer

About the Position

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.

Responsibilities

• 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.

Requirements

• 4+ years in data engineering, with real unstructured / semi-structured work (not only warehouse modeling)
• 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

Skills

• Strong Python and solid SQL
• Unstructured-data pipelines: transcripts, mail, chat, documents - parsing, normalization, 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

We Offer

• Part-time, 20-hour-a-week engagement.
• Remote work from Poland, Hungary, Romania, or Spain.

About the Company

Neurons Lab is a company dedicated to leveraging AI to solve complex data challenges for leading organizations. We pride ourselves on our innovative approach and commitment to excellence.