Product Data Analyst

Responsibilities:

• Data-Driven Decision Support
• Define and maintain key product indicators and a structured metrics tree to ensure consistent measurement across exchange and trading operations.
• Prepare, visualize, and clearly present data needed for informed and timely decision-making.
• Build informative dashboards and reports in Tableau for product, operational, and leadership teams.


• Operational Efficiency & Quality Monitoring
• Monitor product and trading performance by analyzing core metrics, feature output, execution quality, and market behavior.
• Identify inefficiencies, anomalies, or risks — including suboptimal liquidity providers, algorithm behavior issues, or operational bottlenecks.
• Evaluate the performance of trading algorithms and extract actionable insights to improve execution quality and profitability.


• Advanced Analytical Work
• Conduct statistical and quantitative analyses, including back-tests, regressions, and time-series evaluations to validate hypotheses and support product improvements.
• Perform ad-hoc deep-dive analyses based on business and operational requests.
• Prepare new analytical reports and implement alerting mechanisms for key metrics and deviations.


• Cross-Team Collaboration
• Work closely with product and engineering teams to define analytical requirements and support a data-driven approach across the organization.

Requirements:

• 3+ years of proven experience as a Data Analyst, Business Intelligence Analyst, or similar quantitative role.
• Direct, hands-on experience (2+ years) analyzing data related to trading, exchange, and liquidity operations in financial markets (e.g., FX, Crypto, Securities, Commodities). This includes:
• Analysis of Order Book data and tick-level trading data
• Analyzing metrics related to execution and price quality, slippage, latency, spread, markups and trading volumes
• Greatly desirable - experience with Market Microstructure concepts
• Experience working in a company with product management and data-driven approach, understanding of product metrics, and participation in their development.


Technical Proficiency:
• Expertise in SQL for complex querying, data extraction, and performance optimization (writing efficient queries on large datasets is critical).
• Advanced proficiency in Python (or R) for data manipulation, statistical analysis, and modeling.
• Demonstrated ability to create high-impact, interactive data visualizations and dashboards using tools like Tableau, Power BI, or similar.


Analytical Skills:
• Strong statistical foundation and ability to apply statistical methods to product and business problems (e.g., A/B testing, back-testing, regression analysis, time series analysis).
• Proven ability to identify, analyze, and interpret complex data patterns, trends, and potential issues within trading data.


Domain Knowledge:
• Solid understanding of financial products and market concepts (e.g., markups management, margin, P&L calculation, bid/ask spread, market depth, execution types, etc).
• Familiarity with data integrity, reconciliation, and validation processes for sensitive financial data.

Nice to have:

• Quantitative Analysis Background:
• Experience in developing, back-testing, or validating execution strategies or pricing models (e.g., using historical market data).
• Familiarity with risk metrics specific to trading (stress testing, exposure limits).
• Experience working with data from High-Frequency Trading (HFT) systems or low-latency exchange environments.