Head of Data

1. Data strategy and architecture2. Market intelligence data coverage3. Data quality and trust4. AI system integration5. Analytics and proprietary signals6. Leadership

7+ years in data engineering, analytics engineering, data science, or data platforms2+ years leading data teams or owning a data functionStrong experience building production data pipelinesStrong SQL and Python skillsExperience with modern data stacks (warehouses, lakehouses, orchestration, APIs, observability)Ability to design clean schemas and data contractsStrong product intuition: understanding how data drives decisionsComfortable in ambiguous, fast-paced environments Strongly preferred Experience with financial or market dataExperience with alternative or blockchain-style data sourcesFamiliarity with derivatives, order books, or volatility dataExperience with data providers and aggregators in financial marketsExperience integrating data into AI/LLM or agent systemsExposure to macroeconomic datasetsExperience with data licensing and commercial constraintsExperience building confidence scoring or data quality systems

We are building an AI-native market intelligence and trading analysis platform. Our product helps users understand financial markets through live data, research workflows, portfolio context, macro indicators, news, and social intelligence.The core challenge is not simply collecting more data. It is turning fragmented market information into trusted, timely, explainable decision support.We are hiring a Head of Data to own the data layer that powers analytics, insights, research workflows, and future institutional-grade products. Role mission Build and lead the data function that makes our market intelligence product accurate, defensible, fast, and differentiated.You will own the strategy and execution for sourcing, validating, structuring, monitoring, and productizing financial and alternative market data across multiple domains (market, macro, on-chain equivalents, derivatives, flows, news, and social signals).The ideal candidate can move between data engineering, analytics, product thinking, and domain understanding. You should be comfortable asking: Is this source reliable?Is this metric fresh?What does this data actually prove?What does it fail to prove?How should an AI system reason with this data?How do we prevent hallucinated or weak conclusions?