Head of Data
About the company
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?
Responsibilities
1. Data strategy and architecture
2. Market intelligence data coverage
3. Data quality and trust
4. AI system integration
5. Analytics and proprietary signals
6. Leadership
Required experience
- 7+ years in data engineering, analytics engineering, data science, or data platforms
- 2+ years leading data teams or owning a data function
- Strong experience building production data pipelines
- Strong SQL and Python skills
- Experience with modern data stacks (warehouses, lakehouses, orchestration, APIs, observability)
- Ability to design clean schemas and data contracts
- Strong product intuition: understanding how data drives decisions
- Comfortable in ambiguous, fast-paced environments
Strongly preferred
- Experience with financial or market data
- Experience with alternative or blockchain-style data sources
- Familiarity with derivatives, order books, or volatility data
- Experience with data providers and aggregators in financial markets
- Experience integrating data into AI/LLM or agent systems
- Exposure to macroeconomic datasets
- Experience with data licensing and commercial constraints
- Experience building confidence scoring or data quality systems