Top Data & Analytics Jobs in Chicago, IL

Owns fraud and market-abuse detection models, rules, and partner-facing surveillance reporting across trading data. Investigates wash trading, collusion, hedging, reverse trading, and other suspicious activity; calibrates thresholds, validates false positives, maintains data quality, and produces evidence-based findings. Requires strong SQL, cloud data warehouse experience, futures and derivatives knowledge, anomaly detection expertise, and the ability to communicate findings to partners and executives.

Designs and manages advanced data architectures, pipelines, ingestion frameworks, and data quality processes. Develops scalable data solutions across cloud, warehouse, lakehouse, and distributed computing platforms. Builds data consumption methods such as APIs, database views, and extracts while managing lineage, privacy, compliance, and transformation rules. Collaborates cross-functionally to improve data sourcing, accessibility, reliability, and processing efficiency.

Own customer data migrations from legacy systems through QA and launch. Clean, map, import, and validate event, customer, order, donation, ticketing, and marketing data. Configure and troubleshoot integrations, APIs, webhooks, sync failures, and production data issues using SQL, logs, dashboards, and administrative tools. Create migration playbooks, documentation, validation processes, and rollback plans while partnering with customers, support, product, and engineering.

Build data pipelines and relational data models using Python and SQL; clean operational data, support scenario analysis, and create dashboards for PMO leadership. Explore LLMs and machine learning for text analysis and data-quality automation. Gain production experience with testing, documentation, maintainable code, and data-driven decision support in a trading environment.

Develops fraud rules, models, dashboards, and strategies to reduce fraud losses. Analyzes emerging fraud trends, monitors KPIs, conducts ad hoc analysis, supports production deployments, and communicates findings to technical and business stakeholders. The Thursday-through-Monday schedule requires weekend availability, with a hybrid arrangement based in Chicago.

Leads data architecture, data warehouse design, ETL development, and scalable analytics solutions using cloud and modern data technologies. Develops Python, SQL, and JavaScript code; optimizes distributed systems and CI/CD pipelines; troubleshoots production systems; evaluates emerging technologies and AI tooling; collaborates with technical and business stakeholders; and mentors junior engineers.

Supports strategic third-party delivery partnerships through delivery and marketplace data analysis, operational performance monitoring, reporting, and business reviews. Identifies trends, recurring issues, and improvement opportunities; documents findings and develops recommendations. Collaborates with internal stakeholders and external delivery providers, participates in meetings, and presents analytical insights. The internship also includes mentorship, leadership development, networking, and project-based learning.

Conduct quantitative research to design, implement, and deploy mathematical trading models. Build custom tools for exploratory analysis, apply rigorous quantitative techniques to solve complex problems, and collaborate with traders and developers to productionize models while maintaining best practices.

Develops Tableau dashboards, writes optimized SQL, and supports analytics initiatives using large datasets. The intern collaborates with business and technical teams to gather requirements, troubleshoot data issues, and improve access to internal data. Responsibilities include contributing to data warehouse design, maintaining ETL processes, coding, testing, debugging, documentation, and using AI-assisted development tools such as Claude Code. The role operates in a regulated, agile environment and supports data-driven decision-making.

Performs Structured Finance security mappings, data quality controls, reconciliations, regulatory reporting checks, and dataset validation. Maintains metadata, data dictionaries, and data lineage for governance and AI-ready initiatives. Investigates and documents data issues while collaborating with Data, Credit Operations, Technology, and global stakeholders. Supports accurate, complete, timely, and compliant ratings and regulatory datasets.