Head of Data Platform

WhiteBIT; Spain; On-site; EUR 150,000 - 210,000; Full time

Job summary

WhiteBIT in Spain seeks a Head of Data Platform to lead the design and deployment of a scalable data warehouse and real-time BI for trading metrics. You will own end-to-end data pipelines, P&L data, and analytics delivery across services, with strict data quality and governance.

You will manage a team of about 10 engineers/analysts, partner with quantitative researchers, and ensure secure, auditable data access while aligning with business goals and regulatory requirements.

Qualifications

• MSc in computer science, mathematics, statistics, physics or quantitative field; PhD is a plus.
• 5+ years in data engineering/analytics with leadership 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

Responsibilities

• 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 metrics, quoting uptime, external benchmarks and FX feeds
• Close the data debt: gaps in realized-P&L coverage, history backfills, monitoring services — restore or replace
• Set and hold data freshness targets and pipeline-health alerting; own schemas and documentation
• Own analytics delivery end-to-end: KPI rulebook, 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 reviewed and challenged before they reach management; sign-offs from quant/desk stakeholders
• Hire leaders-first and onboard the technical team (~10 people) per the approved hiring plan
• Single interface to exchange requirements with analytics/quant stakeholders; Jira-based roadmap delivery
• After the build: run the platform as a service to the direction — researcher tasks, new metrics and recalibration

Skills

SQL

Python

Data pipelines

Streaming

Data quality

Leadership

Metrics design

Education

MSc in Computer Science or quantitative field

PhD (a plus)

Tools

ClickHouse

Kdb+

Kafka

Kaiko

Amberdata

Job description

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.