Business Intelligence Analyst

MUST:

• 3+ years of experience in business intelligence analyst experience (ideal is within financial trading environment)
• Ability to analyze data like: revenue data, market quality data, trading data and figuring out insights from that data to see what adjustments we need to do (i.e. customers decreased trading by X amount because of an increased fee, revenue went down because of XYZ)
• Bachelors degree in financial engineering, mathematics, statistics, computer science- must something math related
• Expert level in Python and SQL
• Ability to slice and dice data + come to conclusions
• SQL skills: joins, aggregations, window functions, CTEs, and query optimization.
• Experience working with BI tools such as Looker (they use), Tableau, Power BI, Mode, Sigma, or similar platforms for reporting/dashboarding
• Ability to independently pull, clean, validate, analyze, and interpret data.

PLUS:

• Experience with the BI tool called Looker + trading experience
• Masters in something math related
• Redshift and Postgres experience
• Experience working with cloud data warehouses such as Amazon Redshift, Snowflake, BigQuery, or Databricks.
• Experience in financial markets, trading, exchanges, market making, brokerage, crypto, fintech, or capital markets.
• Familiarity with market structure concepts such as bid/ask spreads, top-of-book, depth-of-book, liquidity, slippage, fill rates, order books, execution quality, and market share.
• Experience analyzing liquidity provider, market maker, or execution quality data.
• Experience building executive-level reporting, board reporting, or investor-facing metrics.
• Experience working with product, sales, trading, risk, compliance, or operations teams.
• Comfort working with imperfect datasets and building practical solutions without waiting for perfect data infrastructure.

DAY TO DAY:

Our client, a digital trading firm based out of Chicago, is seeking a highly analytical and self-directed Business Intelligence & Market Quality Analyst to help us transform complex trading, client, and operational data into actionable business insights.

This role will be responsible for analyzing market quality, market share, liquidity provider performance, client behavior, product performance, and broader company KPIs. The ideal candidate is comfortable working independently with large and complex datasets, building automated reporting, and translating data into clear recommendations for senior leadership.

Our data environment currently includes Looker and Amazon Redshift. The right candidate should be able to autonomously pull data, validate data quality, build repeatable reporting, investigate trends, and identify opportunities to improve business performance.

This is a high-impact role for someone who enjoys working at the intersection of data, markets, operations, and strategy.

Key Responsibilities

• Analyze market quality across trading products, venues, liquidity providers, and client segments.
• Evaluate market maker performance, including quote quality, spread competitiveness, uptime, depth, fill quality, and responsiveness.
• Measure and report on market share, volume trends, client activity, liquidity trends, and revenue drivers.
• Build, maintain, and improve dashboards and reporting in Looker.
• Write and optimize SQL queries against Redshift and other data sources.
• Create automated reports for leadership, sales, trading, operations, risk, and product teams.
• Identify meaningful trends, anomalies, and opportunities within trading and business datasets.
• Translate complex data into clear, concise insights and recommendations.
• Partner with internal stakeholders to define KPIs, reporting requirements, and analytical frameworks.
• Develop repeatable processes for recurring business reviews, market quality reviews, client performance reviews, and board-level reporting.
• Improve data quality by identifying inconsistencies, missing fields, unreliable metrics, or gaps in existing reporting.
• Support ad hoc analyses for strategic initiatives, client conversations, liquidity provider evaluations, and product launches.
• Build scalable analytical models and reporting processes that reduce manual work over time.
• Proactively investigate business questions without requiring step-by-step direction.