Top Senior Data Management Jobs in Chicago, IL

Lead and grow a data platform engineering team to build and maintain a petabyte-scale data lake and infrastructure. Drive platform roadmap, scale systems globally, collaborate cross-functionally, and embed best practices for security, privacy, and AI transformation.

Lead a team evaluating new and existing data assets, standardize due-diligence and analytical methodologies, validate data quality and business value, partner with Product and Technology to support onboarding and OneTru cataloging, establish governance and lineage standards, and coach team members to drive data-driven product and revenue decisions.

Lead a remote analytics team to deliver dashboards, data products, and decision-support tools. Oversee project plans, stakeholder engagement, data platform modernization, ETL/automation, Tableau/Power BI deployments, statistical modeling, QA, metadata, and staff mentoring to support federal social service programs.

Lead validation of enterprise analytical tools: refine validation framework, plan and execute projects, assess methodology quality and risk, prototype best practices, communicate findings, and coach junior staff.

Leads public financial management data analysis, fund-flow and expenditure reconciliation, fiduciary risk assessment, and verification support in Uganda. Produces dashboards, exception reports, risk flags, and evidence summaries from financial, HRIS, and administrative data. Supervises and trains analysts and verification teams, coordinates with government and project stakeholders, maintains audit trails, and identifies issues requiring targeted field verification.

Lead design and rollout of a fit-for-purpose data governance program: manage data lifecycle, implement governance frameworks and tools, drive data catalogs/lineage, upskill stewards, define KPIs, and partner cross-functionally to improve data quality, compliance, and usability.

Leads the design, delivery, and reliability of an enterprise data platform. Manages and mentors data engineers, oversees architecture and scalable pipelines, establishes engineering standards, and drives platform modernization. Responsibilities include incident response, monitoring, governance, security, AI-assisted development, automation, and GCP cost optimization. The role requires hands-on technical leadership across data modeling, cloud infrastructure, CI/CD, batch and streaming architectures, and stakeholder collaboration.

Lead a team delivering enterprise-wide model risk reporting and AI-driven automation. Build and deploy scalable ML/AI solutions, conversational reporting, multi-agent workflows, and centralized data integrations. Drive analytics strategy, validate models and data, improve reporting and operational efficiency, and collaborate across business units.

Leads enterprise analytics strategy, data integration, governance, reporting, dashboards, forecasting, and advanced business analysis. Manages analysts and partners with Sales, Pricing, Finance, Product Management, Operations, and executive leadership to translate complex data into actionable recommendations. Oversees Power BI dashboards, KPI frameworks, data quality, predictive modeling, and reporting automation to improve growth, profitability, pricing, and operational performance.

Leads the architecture, implementation, and evolution of enterprise data platforms, including Master Data Management, data lakes, lakehouses, and cloud data services. Manages data engineers and developers, establishes engineering standards, oversees Azure-based integrations and pipelines, and owns data applications, databases, security, governance, and production support. The role also directs technical strategy, manages multiple initiatives, and communicates architecture and delivery plans to stakeholders.

Lead a scrum team of 5–7 data engineers to design, build, and operate Azure Databricks Lakehouse solutions. Set technical standards, drive data quality, oversee ETL/streaming pipelines, code reviews, CI/CD, and partner with architects and business stakeholders to deliver scalable, governed data products.

Lead and grow a data engineering team that builds and operates ingestion, transformation, orchestration, and serving layers and a self-serve analytics substrate. Own data quality, observability, cost efficiency, SLAs, and engineering practices while partnering with Data Science, BIE, Analytics, and Product to deliver reliable, reusable data products.

Manage and improve corporate giving data systems: build and maintain Tableau/Excel dashboards, respond to ad hoc data requests, clean and govern community data, administer Bonterra/CyberGrants, and lead project management for giving programs and strategic initiatives while partnering with enterprise stakeholders.

Leads enterprise business intelligence and analytics strategy, owning the vision, roadmap, KPIs, and adoption measures for insight products. Manages data engineering and analytics teams, partners with senior leaders, translates business questions into analytical solutions, and delivers executive-ready insights. Provides technical oversight for data models, pipelines, semantic layers, data quality, scalability, and metric standardization across transformation, sales, operations, sustainability, and other enterprise domains.

Lead a small data engineering team and own market data platforms from ingestion through delivery. Build and maintain KDB+/Q time-series systems, ensure data correctness, low-latency decoding, replay/recovery, and reliable distribution to downstream consumers. Write production code, review designs, improve observability, and prepare the platform for AI-driven consumers.

Leads the enterprise analytics organization, managing Analytics Engineers, Data Analysts, and BI Developers. Defines analytics strategy, delivers executive dashboards and operational reporting, establishes KPI and governance standards, builds semantic models and curated datasets, and drives self-service BI adoption. Partners with Engineering, Product, Finance, Operations, Customer Success, and executives to translate business needs into trusted analytical solutions while developing talent and prioritizing analytics initiatives.

Leads the enterprise analytics organization, managing Analytics Engineers, Data Analysts, and BI Developers. Defines analytics strategy; delivers executive dashboards, KPI scorecards, operational reporting, semantic models, and self-service BI. Establishes data governance and metric standards, ensures data quality, partners with Engineering, Product, Finance, Operations, and executives, and translates business questions into actionable analytical solutions. Mentors analytics talent and drives prioritization, adoption, and measurable business outcomes.

Analyze medical claims (inpatient, outpatient, SNF, rehab, professional, DME) to identify utilization and root causes. Pull/integrate data, perform QA, present findings, maintain attribution methodology, coordinate stakeholders, and develop tools. Primary coding: Hive, SQL, Python.

Leads cross-functional data management and analytics projects for pharmaceutical and life sciences clients. Responsibilities include data warehouse development, ETL and schema design, data analysis, process optimization, client communication, project management, business development, and mentoring junior staff. The role also provides strategic guidance, develops reporting solutions, manages timelines and resources, and contributes to internal data platform initiatives.

Own the Industrial platform’s data infrastructure, datasets, dashboards, models, and AI-enabled analytical workflows. Deliver investor and occupier business-development insights, supply-chain mapping, surveys, executive reporting, presentations, and pursuit support. Partner with Research, Sales Enablement, technology, operations, brokers, and leadership to improve data integrity, self-service reporting, platform capabilities, and automation. Coordinate cross-functional initiatives and translate quantitative analysis into actionable narratives for senior leaders and clients.

Manages the enterprise Data Governance and Analytics Center of Excellence, leading staff, governance initiatives, analytics strategy, maturity modeling, budgets, vendors, and planning. Coordinates data quality, stewardship, privacy, acquisition, and release programs while partnering with data scientists, engineers, technology governance teams, and business stakeholders. Develops executive presentations, aligns enterprise definitions and algorithms, establishes standards, and ensures analytics projects and business cases meet organizational objectives and deadlines.

Manage the day-to-day functionality, integrity, governance, and reporting of spatial, CAD floor plan, occupancy, and utilization data across a corporate real estate portfolio. Administer the space management system, enforce standards and user controls, support CAFM integrations, monitor data quality and allocation processes, provide training, recommend process improvements, and advise space planning teams on consistent workflows and FF&E solutions.

Leads data strategy and analytics for the Risk/Credit team, translating complex datasets into insights for Credit, Operations, Marketing, Product, and Finance. Responsibilities include automating Python/R workflows, building Tableau dashboards, partnering with Data Engineering on Databricks workflows, conducting strategic analyses, monitoring data quality, presenting recommendations to leadership, and documenting analytical methodologies. The role requires strong SQL, cloud data warehouse, visualization, analytics programming, distributed computing, and version-control expertise.

Leads end-to-end data architecture, ETL pipelines, data models, quality frameworks, monitoring, and BI platform operations. Manages Tableau infrastructure, drives project plans and agile sprint delivery, establishes AI-enabled analytics capabilities, and partners with cross-functional stakeholders. Coaches and manages data engineering team members while ensuring data availability, accuracy, security, documentation, and continuous improvement.

Lead the master data management function, set data standards and governance, manage MDM analysts, ensure data quality across item/vendor/pricing/location domains, partner with cross-functional teams, support system implementations and migrations, and drive continuous improvement in retail data processes.