Data engineer unstructured data and AI pipelines

The project builds a private, access-scoped context layer over a European private investment group's data, with AI skills and agents built on top.

• Set up call, email, Slack, and messenger ingestion with speaker attribution and reversible opt-out
• Backfill historical email, Slack, board protocols, decks, and portfolio updates, ensuring they are parsed, deduplicated, and correctly dated
• Build document parsing for PDFs, scanned board packs, spreadsheets, slide decks, and forwarded attachments
• Implement identity and entity resolution across communication tools, calendars, portfolio companies, and CRM records
• Build chunking and embedding pipelines and load vector and graph stores behind the architect-defined ontology
• Implement incremental sync through the connector layer, handling edits and deletions without full re-crawls or silent drift
• Attach access scope and provenance to every record during ingestion to support downstream permission-aware retrieval and audits
• Run PII detection, redaction, and retention logic; provide the client's security function with evidence of what is stored, where, and for how long
• Orchestrate monitored, repeatable pipelines with Airflow, Step Functions, or equivalent, and alert when a source stops flowing
• Control cost and latency at volume through batching, incremental embedding, and storage tiering; report unit economics
• Write runbooks so the client's team can operate the system after handover

• 4+ Years in data engineering, including unstructured or semi-structured data work beyond warehouse modelling
• Demonstrated experience integrating multiple third-party APIs into a coherent store, including historical backfill
• Experience handling sensitive personal data in a regulated or security-sensitive environment
• Comfortable working as the only data engineer in a small 2.5-FTE pod, at part-time allocation, without hand-holding
• Strong Python and solid SQL
• Experience with unstructured-data pipelines for transcripts, mail, chat, and documents, including parsing, normalisation, and deduplication
• Experience with chunking strategies, vector stores such as pgvector, Open Search, or Pinecone-class systems, and graph-store loading
• Experience integrating APIs and connectors at scale, including Google Workspace or M365, Slack, and CRM; rate limits, pagination, incremental cursors, and webhooks
• Experience with entity resolution or record linkage, deterministic and fuzzy, without a clean shared key
• Experience with Airflow, Step Functions, or equivalent orchestration and idempotent, restartable jobs
• AWS and/or GCP data stack experience and comfort with private or VPC deployments
• Practical knowledge of PII detection, redaction, encryption, and retention
• Clear written English; able to prepare handover documents and work asynchronously in a small distributed pod
• Knowledge of GDPR as applied to employee-generated data and EU data residency across multiple jurisdictions
• Knowledge of data lineage, provenance, and audit patterns
• Understanding of how retrieval quality depends on ingestion quality and sufficient RAG knowledge to make upstream choices
• Будет плюсом: experience building pipelines feeding an LLM or retrieval system, Well-Architected security and cost practices, awareness of financial-services expectations

• Part-time engagement, 20 to 30 hours a week; allocation may flex above 0.5 FTE during Capture and Connect and settle back afterwards
• Approximately eight to ten two-week sprints overall, with workload front-weighted to the first four or five Active project; start ASAP