---
title: 'Data Engineer (UA/RU Language speaking) at Neurons Lab'
canonical: 'https://feeny.ai/job/data-engineer-ua-ru-language-speaking-neurons-lab-poland-3dy117s3b1pz'
type: 'job'
last_seen: '2026-09-08'
---

# Data Engineer (UA/RU Language speaking) at Neurons Lab

- **Company:** Neurons Lab
- **Location:** Poland
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-07-30
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/neurons-lab.com/ccbcdfbc-1825-43f2-ae99-4d470872c77b

## Job description

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

## About Neurons Lab

## Company Overview
- **One-liner**: Neurons Lab is an AI engineering partner that helps Financial Services organizations adopt AI and build production-grade agentic systems, from initial use case definition through deployment and continuous delivery.
- **Entity Type**: Private (Bootstrapped / Self-funded boutique firm)
- **Headquarters**: London, United Kingdom (International House, 64 Nile Str, London, N1 7SR)
- **Founded**: 2019
- **Founders**: Igor Sydorenko (CEO & Co-Founder) and Alex Honchar (CTO & Co-Founder)

## Core Business
- **Primary industry/industries**: AI Engineering & Consulting, focused exclusively on Financial Services (banking, capital markets, wealth management, insurance)
- **Target customers**: B2B — Fortune 500 financial institutions, large banks, wealth management firms, insurance companies, and adjacent regulated industries
- **Mission or purpose statement**: To help Financial Services organizations move from AI-curious to AI-enabled by building capability and deploying systems across core workflows — translating AI ambition into secure, scalable solutions that deliver commercial value.

## Products & Services
- **Custom AI Agents**: Deploy custom AI agents from discovery through production, enabling automation across core workflows with full auditability, traceability, and governance controls built in from the start. Decision logic is aligned with business rules and policies, designed for high-value workflows where accuracy, compliance, and scale are critical.
- **AI Training & Enablement**: Build AI fluency across teams, from engineering to the C-suite, through hands-on training grounded in the client's core workflows. Separate tracks for business and technical teams, with governance, compliance, and risk embedded from the start.
- **Continuous AI Delivery**: Post-deployment support with Forward-Deployed Engineers working alongside client teams to ensure systems continue to evolve. Governance, monitoring, and performance tracking are built in from the start, with AI adoption compounding across workflows over time.

## Market Standing
- **Valuation/Market Cap**: Not publicly available (private, self-funded boutique firm)
- **Key Metric**: Total Funding — Not disclosed (bootstrapped); the team reports 100+ AI implementations since 2019, including with Fortune 500 firms, and a team of 50+ AI engineers, architects, and analysts across Europe
- **Notable Investors/Partners**: Anthropic (partner), AWS (Advanced Partner, among first 15 to achieve AWS GenAI Competency in Agentic AI), Google Cloud (Partner)
- **Growth Signals**: Achieved AWS AI Competency in the Agentic AI category; named clients include HSBC (aligned 50+ senior leaders on AI deployment and governance) and Visa (scaled marketing operations across 9+ markets with LLM-based content system); works with leading Asian banks on agentic AI assistants for relationship managers

## Competitive Advantages
- **FSI Domain Expertise**: Deep specialization in Financial Services (banking, capital markets, wealth management, insurance) means no translation layer between business intent and technical execution. Compliance, governance, and risk are built into system design from the start.
- **Embedded Co-Creation**: Unlike traditional consultancies, they co-create with client teams through embedded delivery, working alongside internal stakeholders to transfer knowledge and expand impact from inside the organization.
- **Accelerated Delivery**: Pre-built FSI agent components and proven deployment patterns allow delivery of working, production-grade systems in weeks rather than months.
- **Boutique Senior Teams**: Small, senior teams (50+ engineers/architects) rather than large junior-heavy teams, enabling high-quality, fast execution.

## Strategic Focus
- Expanding agentic AI adoption within regulated financial environments, focusing on production-grade systems that operate within existing governance frameworks
- Building on partnerships with Anthropic, AWS, and Google Cloud to deliver cutting-edge AI solutions
- Deepening presence in wealth management, retail/corporate banking, private banking, SME banking, and insurance segments
- Scaling through a two-tier talent model (Core Team + Talent Network) to remain agile while growing

## Why Work Here
- **Remote-first culture**: Work from anywhere, with flexible hours — "design your day" philosophy
- **Benefits**: Unlimited PTO, flexible schedule, work on projects incorporating the latest AI technology (Anthropic, AWS, GCP)
- **Culture**: 7 guiding principles including ownership, genuine connections, and leadership development. Small, senior teams with high autonomy and low bureaucracy
- **Growth**: Opportunity to work on cutting-edge agentic AI for Fortune 500 financial clients, with clear paths to leadership roles
- **Team structure**: Two collaboration options — Core Team (full-time, benefits-eligible) or Talent Network (project-based, minimum 0.33 FTE)
- **Interview process**: Fast, streamlined — application → screening interview → validation interview → qualification interview → reference check

## Sources
1. [neurons-lab.com](https://neurons-lab.com/about/)
2. [neurons-lab.com](https://neurons-lab.com/)
3. [neurons-lab.com](https://neurons-lab.com/careers/)
4. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/neurons-lab.com)
5. [neurons-lab.com](https://neurons-lab.com/careers/core-team/)

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