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 general 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:

— A

📌 Head of Data Platform (España)
🏢 WhiteBIT
📍 España