Description

As the Head of Data, you will lead the strategic direction of the trading data ecosystem, ensuring data quality and reliability while driving initiatives that enhance trading performance and risk transparency. This role involves collaborating with various teams to build a robust data architecture and foster a culture of data excellence across the organization.

Requirements

• •Deep technical expertise in data engineering and ML workflows
• •10+ years of experience in data engineering, data architecture, or data platform leadership roles
• •Proven experience in building and managing trading or financial data platforms
• •Strong understanding of market data feeds, trade lifecycle, and financial data models
• •Expertise with modern data technologies (ClickHouse, PostgreSQL, Kafka, or equivalents)
• •Experience implementing ML Ops frameworks (e.g. MLflow, Kubeflow, Prefect, or SageMaker)
• •Deep understanding of ETL/ELT pipelines, streaming architectures, and data lifecycle management
• •Experience working in Kubernetes and cloud-native environments (AWS preferred)
• •Sound knowledge of data governance, security, and compliance best practices
• •Demonstrated ability to lead cross-functional teams and influence data-driven decision-making
• •Background in crypto markets, financial trading, quantitative research environments or other data-intensive industries
• •Exposure to real-time analytics, monitoring, and observability tools (Grafana, Prometheus, DataDog etc.)
• •Understanding of DataOps, FinOps, and cost-optimised data management practices
• •Familiarity with distributed systems and high-throughput data processing (e.g. Ray, Dask, Spark)
• •Experience with feature engineering, model lifecycle management, and data-driven research environments (e.g. JupyterHub)
• •Self-awareness and powerful communication skills
• •Passion for crypto and collaborative ownership

Tasks

• •Define and execute a data strategy focused on trading and market data
• •Collaborate with the Architecture Team to build a compliant data architecture
• •Drive the data excellence agenda, embedding reliability across the data platform
• •Prioritize initiatives to enhance trading visibility and business performance
• •Identify opportunities for data and ML to improve trading performance
• •Lead the implementation of cloud-native data platforms for trading datasets
• •Standardize data ingestion and modeling patterns for system interoperability
• •Align data platform evolution with enterprise architecture principles
• •Ensure data systems meet performance, cost, and scalability targets
• •Drive adoption of data engineering best practices for operational excellence
• •Enforce robust data governance across the data lifecycle
• •Partner with Security and Compliance teams for adherence to relevant frameworks
• •Ensure consistent metadata, documentation, and versioning across the data stack
• •Champion a data-driven culture with transparency and auditability
• •Build, lead, and mentor a high-performing Central Data Team
• •Partner with various teams to deliver trusted and scalable data solutions
• •Foster a culture of data reliability and continuous improvement
• •Define measurable data KPIs for platform improvement
• •Promote collaboration between data producers and consumers
• •Champion a data-driven and experimentation-driven culture

Tools & Technologies

Benefits