Head of Data Platform (EU)

We are the creators of a new fintech era!
Our mission is to revolutionize the world by making blockchain technology accessible to everyone in everyday life. WhiteBIT is a global team of more than 1,500 professionals united by a shared vision of shaping the future.
We are building our own blockchain ecosystem to ensure maximum transparency and security for over 8 million users worldwide. Our cutting-edge solutions, rapid adaptation to market challenges, and technological excellence set us apart from traditional companies.
Our official partners include Juventus, FC Barcelona, Lifecell, FACEIT, and VISA.
Join us as a Head of Data Platform.

Requirements:
Education & Experience:
— MSc in computer science, mathematics, statistics, physics or another quantitative field; PhD is a plus, not a requirement — research-grade methodology sits with the Quantitative Researcher
— 5+ years in data engineering / analytics, incl. lead / management experience; built a trading / financial data platform from zero at a crypto exchange, HFT / electronic-trading firm, market maker, or trading fintech. Bank / enterprise-DWH background alone does not qualify
— Personally carried production ownership of money data — trades, balances, positions, P&L — where a data error is a financial error
Core Technical Skills — data platform:
— Hands-on today: designs and ships pipelines personally — advanced SQL and Python; ClickHouse (or kdb+ / comparable columnar OLAP) in production
— Streaming and batch in one architecture: Kafka or equivalent, event feeds with second-grade freshness alongside hourly aggregates; trading-day logic — metrics counted within a trading day that starts and ends at a fixed morning hour, with an automatic daily reset
— Optimizes data footprint and computational overhead across every stage: selects the tech stack, partitioning logic, and execution flow based on cost-to-performance impact
— Data-correctness engineering: reconciliation, idempotent backfills, late / duplicate event handling, data-quality monitoring and lag alerting
— Vendor and API integration: institutional market-data providers (Kaiko / Amberdata class), exchange APIs, FX feeds; schema design and documentation others can build on
Core Technical Skills — analytics & applied math:
— Applied statistics at a working level: distributions, hypothesis tests and confidence intervals, correlation vs causation, time-series and aggregation pitfalls (session boundaries, survivorship, outliers) — enough to catch a methodological error in an analyst's query or notebook, and to have designed or reviewed metric / experiment methodology in past roles
— Metric and semantic design: one written definition per metric (P&L, uptime, spread, fill rate) enforced across every report; analytical data models (marts / star schemas) that make those definitions computable and cheap to query
— BI stack governance: Tableau / Grafana — performance, access control, versioned metric definitions; reviews dashboards for correctness rather than building them all himself
— ML literacy: understands when a model is — and is not — needed and can supervise applied work; research-grade modeling remains the Quantitative Researcher's mandate
Market & Domain Knowledge:
— Trading data from practice, not textbooks: maker/taker, fees and rebates, perpetual funding, realized / unrealized P&L, inventory and exposure, order-book depth and spread, slippage, quoting uptime
— Understands the consumers: how analysts, quants and desk heads use the data, and how market-quality / compliance metrics feed liquidity-provider terms
— Has built and run a mixed organisation of data engineers and analysts: separate hiring bars and growth tracks, peer review of queries and dashboards as a standard, methodology documented before a number ships
— Partners with quantitative research: turns the Researcher's methodology into production metrics and dashboards without distorting it; runs the sign-off process with quant and desk stakeholders
— Treats balances and client data as confidential by default: access control, least privilege, audit trail, no raw-data leaks
— Never ships silently wrong numbers: escalates early, cuts scope consciously, flags uncertainty in the data
Mindset & Soft Skills:
— Player-coach: the first months are personal hands-on delivery; scaling through the team runs in parallel, not instead
— Speaks business: reports status in money and deadlines; prioritizes by P&L impact — which dataset unblocks which decision; no jargon walls with management and stakeholders
— Owner of the direction's technical goals, not of a backlog: takes a business problem, decomposes it into tasks, executes and delegates
Responsibilities — Platform:
— Architect and ship the central data warehouse: every trade, position, balance and P&L across all trading services; an hourly contour for BI plus a real-time contour for monitoring; trading-day logic with automatic daily reset
— Build the data layers behind LP terms: market quality (spread, depth near mid price, fill rate, per-order slippage), quoting-uptime start/stop events per maker and pair, external tier-1 benchmarks and FX feeds for regional fiat pairs
— Close the data debt: gaps in realized-P&L coverage, history backfills, broken monitoring services — restore or replace
— Set and hold agreed data-freshness targets and pipeline-health alerting; own schemas and documentation
— Own analytics delivery end-to-end: the KPI rulebook published and signed, financial / attribution / historical-performance dashboards, compliance screens and provider scorecards
— Enforce a single semantic layer: one definition per metric across every report; methodology documented before a dashboard ships
— Quality gate: numbers are reviewed and challenged before they reach management; sign-offs from quant / desk collected per deliverable
Responsibilities:
— Hire leaders-first and onboard the technical team (~10 people: data engineering, analytics & BI, technical PM) per the approved hiring plan; hand tasks down without losing velocity
— Single interface to exchange tech (requirements, acceptance, freshness targets) and to analytics / quant stakeholders; run delivery in Jira against the approved one-year roadmap
— After the build: run the platform as a service to the direction — researcher-generated tasks, new metrics and pair groups, regular recalibration.

Working terms
Immerse yourself in Crypto & Web3:
Master cutting-edge technologies and become an expert in the most innovative industry.
Work with the Fintech of the Future:
Develop your skills in digital finance and shape the global market.
Take Your Professionalism to the Next Level:
Gain unique experience and be part of global transformations.
Drive Innovations:
Influence the industry and contribute to groundbreaking solutions.
Join a Strong Team:
Collaborate with top experts worldwide and grow alongside the best.
Work-Life Balance & Well-being:
Modern equipment.
Comfortable working conditions, and an inspiring environment to help you thrive.
22 business days of paid leave.
Additional days off for national holidays.