Top Data & Analytics Jobs

Leads and develops a data engineering team responsible for ingestion, Snowflake administration, orchestration, CI/CD, data integrations, and platform reliability. Owns technical strategy, production operations, access controls, cost management, incident response, and engineering standards. Partners with product, analytics engineering, DevOps, and business stakeholders to deliver scalable data solutions. Mentors engineers, sets goals, prioritizes work, and supports organizational growth.

Own Bay FC’s fan analytics narrative and translate business, market, and consumer data into actionable commercial recommendations. Analyze revenue performance, growth opportunities, brand health, audience segments, fan lifecycle behavior, and campaign effectiveness. Partner with Marketing, Ticketing, Merchandise, and senior leadership on analytical projects, business reviews, testing frameworks, and executive-ready insights. Build unified fan profiles, guide audience targeting, and influence organizational strategy through clear data storytelling.

Leads global data operations for robotics and AI training datasets, overseeing collection, annotation, quality control, delivery, vendors, budgets, compliance, and process scaling. Builds and manages distributed teams, establishes operational KPIs, coordinates with clients and technical teams, and ensures training-ready multimodal data meets quality and regulatory standards.

Gather reporting requirements from stakeholders, translate them into report specifications, coordinate with BI team to develop and validate reports, ensure data accuracy and compliance, provide stakeholder support and status updates, and identify process improvements for report creation and delivery.

Conduct end-to-end data analysis, translate ambiguous business questions into actionable recommendations, and support decision-making across company functions. Build and maintain dbt models, tests, documentation, curated tables, semantic-layer metric definitions, and BI reporting. Partner with data engineering and stakeholders to ensure data quality, governance, and self-service access for people and AI agents. Maintain dashboards and identify opportunities across multiple business verticals.

Leads the technical architecture, data infrastructure, analytics, and operational systems supporting a teacher residency program. Maintains dashboards, databases, integrations, user access, compliance systems, surveys, reporting, and predictive monitoring. Uses data to identify at-risk residents, guide interventions, improve matching and placement, and support program decisions. Manages external developers and collaborates with leadership, universities, districts, mentors, and staff. Requires approximately two onsite days weekly in Chicago or Lake County, with additional onsite support during major events.

Leads and develops an Enterprise Decision Science team while personally conducting strategic analyses. Establishes analytical standards, evaluates product and go-to-market initiatives using experiments and quasi-experimental methods, and makes recommendations from limited quantitative and qualitative data. Partners with Product, Engineering, Sales, Rev Ops, Customer Success, and leadership to prioritize analytical work, measure business impact, and communicate findings clearly.

The Product Analyst will manage the product experience, consult clients, collaborate with internal teams, and guide new features in biotech and pharma contexts.

Build targeted donor lists using Grassroots Analytics’ database, fulfill client data requests, provide targeting recommendations, and support fundraising strategists with campaign and nonprofit onboarding, training, and client communications. The intern will also assist with internal projects and develop relationships across departments. This is a full-time, in-office internship in Washington, D.C., running January 25 through April 16, 2027.

Owns the end-to-end lifecycle of core financial data products, including security master, market, trading, and reference data. Builds and monitors automated pipelines, implements data quality checks and observability, resolves operational issues, and automates manual workflows. Partners with quants, trading desks, and technical and non-technical stakeholders to create reliable, point-in-time-correct datasets and maximize their value across the organization.