Top Data & Analytics Jobs in Chicago, IL

Develop data solutions, business intelligence tools, dashboards, and analytical insights while managing data quality, metadata, lineage, security, access governance, and data quality monitoring. Partner with business and technology teams to address complex data problems and enable self-service analytics. The role requires scripting, querying, visualization, AWS development, and data governance expertise.

Leads data governance, risk mitigation, analytical tool validation, and program development for Global Payment Networks. Responsibilities include planning validation projects, assessing analytical methodologies and tool risks, researching industry practices, preparing reports and presentations, and coaching junior staff. The role requires managing concurrent projects and applying data analytics, risk management, statistical, and project management expertise to develop enterprise-wide controls and processes.

Leads data engineering development across Agile teams, designing, building, testing, implementing, and supporting cloud-based solutions. Manages developers working with machine learning, distributed microservices, full-stack systems, big data, real-time streaming, data warehouses, and NoSQL technologies. Uses Java, Scala, Python, cloud platforms, Redshift, Snowflake, and related tools while conducting code reviews, performance tuning, mentoring engineers, and collaborating with product managers.

Leads enterprise ontology and data-modeling strategy, governance, integration, standardization, and roadmap development. Manages teams creating semantic models, taxonomies, linked data, and reusable capabilities while partnering with technology, machine learning, and business stakeholders. Advocates W3C standards, supports knowledge organization workshops, establishes modeling guidelines, monitors industry trends, and mentors team members. Requires expertise in metadata, ontologies, linked data tools, graph technologies, SQL or SPARQL, and semantic standards.

Manage data analysis for transaction monitoring, risk identification, and compliance. Analyze high-volume transaction data using SQL, Python, Splunk, and visualization tools; conduct root-cause investigations; design risk data architectures; identify process and reporting improvements; advise business and product teams; and translate technical findings into executive recommendations. The role also involves stakeholder liaison, regulatory alignment, data integrity, and managing concurrent analytical projects.

Analyze payment, lending, marketing, and credit-risk data to identify business opportunities, build strategic recommendations, develop and monitor statistical models, and support product and pricing decisions. Partner with IT, Sales, Operations, Finance, and other teams to implement solutions, improve performance, and drive profitability. The role requires strong quantitative reasoning, business judgment, communication, leadership, and execution skills in a fast-paced environment.

Lead data strategy, architecture, and modeling for high-volume transactional network platforms. Build monitoring and anomaly-detection frameworks, optimize operational data schemas, align local assets with enterprise data strategy, communicate findings to stakeholders, and mentor technical staff.

Lead analytical efforts to inform product, pricing, marketing, and credit risk strategies. Build and test models, perform SQL-based analysis, develop recommendations, drive implementation with cross-functional partners, and monitor results to improve profitability and customer experience.

Lead data engineering teams in designing, developing, testing, implementing, and supporting cloud-based data solutions. Build distributed, full-stack, machine-learning-enabled systems using Java, Scala, Python, relational and NoSQL databases, streaming technologies, and cloud data warehouses. Collaborate with product managers and Agile teams, conduct code reviews and unit testing, optimize performance, mentor engineers, and adopt emerging technologies and interactive AI tools.

Lead validation of enterprise-wide analytical tools for risk management: plan and execute validation projects, assess methodology quality and tool risk, prototype best practices, communicate findings, and coach junior staff.