Top Data Management Jobs

Lead a 3-4 person analytics team supporting Americas Compliance. Partner with Compliance stakeholders to deliver regulatory reporting, build automated data pipelines, produce monitoring analytics, define KPIs, champion data integrity, and lead automation/tooling projects to increase efficiency and impact.

Lead and grow a data engineering team to build scalable, cloud-native multi-cloud Lakehouse data products using Databricks, Spark, Delta Lake and near real-time streaming. Drive migration from legacy systems, enable AI/ML and GenAI use cases, ensure observability, data governance, CI/CD, and collaborate with product, analytics, security and architecture teams to deliver customer and marketing data solutions.

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 the migration domain to ensure accurate, complete, repeatable migration of customer, account, product, and transactional data. Oversee migration automation/tooling and legacy data hygiene. Own analytical frameworks, data mapping, and reconciliation controls to ensure institutional and regulatory confidence. Lead cross-functional teams and drive product outcomes aligned with business and technology objectives.

The Senior Manager will lead cross-functional data analysis projects, manage teams, validate tools, and mentor junior staff while enhancing technical skills.

Lead design and delivery of reusable, AI-ready data products and interoperable data-engineering artifacts for retail-banking portfolios. Manage data product lifecycles, ensure data integrity and governance, create self-service analytics, and align technology and business teams in a matrixed organization.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Lead a team of 4-10 data engineers to design, build, and maintain scalable ETL and data integration pipelines. Manage Agile delivery, enforce engineering best practices, optimize SQL performance, partner with analytics and product teams, drive automation, and ensure compliance with security and regulatory requirements.

Senior Data Risk Manager providing second-line oversight for enterprise data risk. Conduct risk and gap assessments, advise business and technology partners, embed data governance and quality controls, and influence product roadmaps to ensure regulatory compliance and strong data management practices.

Support engineering teams by planning, organizing, controlling, and submitting contractually required data. Create 30/60/90 day deliverable plans, review deliverables for contractual compliance, present status in program meetings, execute data management processes, and communicate proactively. Strong Microsoft Office skills and ability to obtain Top Secret clearance required.

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.

Manage program data and documentation across the contract lifecycle: compile, review, control, publish, and submit deliverables to meet CDRL/DID requirements. Develop and implement data/configuration management processes, maintain master files, set milestones, report status, and coordinate with stakeholders to ensure timely, compliant deliveries.

Lead redesign of the Customer Success unified data model and AI data strategy. Build and optimize pipelines, integrations (contact center), testing/monitoring, and a documented semantic layer. Manage and mentor a small team, partner cross-functionally on KPIs, governance, and data quality, and ensure CS data is reliable for dashboards, reporting, and AI use cases.

The Data Strategy Manager will lead projects to deliver data strategy solutions in Life Sciences, manage client relationships, and mentor team members.

Lead a team to analyze large-scale datasets, build and validate models, improve reporting and operational efficiency, design data/storage systems, run strategic tests, and collaborate across business units to drive analytics strategy and customer experience.

Lead a product management team for enterprise data platform and data governance. Define strategy, roadmaps, and deliverables; manage program/project lifecycles; partner with stakeholders to build cloud and AI-enabled governance capabilities; apply agile methods; ensure regulatory and audit responses; develop and mentor staff.

Lead and mentor a team of analysts to deliver HR-related analytics and BI solutions. Mine and model large datasets, design tests, improve reporting and operational efficiencies, and collaborate across business units to inform strategy and product decisions.

Lead data governance for the Global Payment Network: implement and enforce policies, ensure data quality, master data management tooling, drive automation and process improvements, collaborate with executives, data stewards and tech teams, and synthesize risk reporting and insights for senior leadership.

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.

Lead a team to design, operate, and scale core data pipelines and analytics infrastructure. Ensure reliable ELT/ETL, dbt-based models, data governance, metric consistency, and cross-functional partnership to deliver trusted self-service analytics and measurable business impact.

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.

Lead development, deployment, and optimization of cloud data platforms and real-time pipelines. Manage cross-functional on-site and offshore teams, ensure production stability, troubleshoot issues, optimize AWS and data warehouse cost/performance, implement CI/CD for pipelines, and integrate AI-driven ops for anomaly detection and predictive data quality.

Lead a product analytics team to measure and improve product and platform performance. Mine and analyze large datasets, build and validate models, implement reporting and BI improvements, consult on data/storage system design, run strategic tests, mentor junior analysts, and collaborate cross-functionally to drive data-informed product decisions.

Lead and scale a data engineering team to build and maintain BigQuery-based ELT pipelines and Medallion architecture. Provide technical direction, hands-on engineering (dbt, Python, Fivetran), enforce governance, data quality, observability, CI/CD and IaC standards, and partner with platform and business domains to deliver a reliable data platform.

Lead a data engineering team building pipelines, transformations, and analytics infrastructure in Azure/AWS/Snowflake. Champion spec-driven, AI-assisted development, ensure secure scalable systems, govern AI-generated code, and partner with product and platform teams.