Top Senior Data Management Jobs

Leads data engineering teams responsible for designing, developing, maintaining, and integrating scalable data systems, pipelines, warehouses, and governance solutions. Provides technical leadership, mentorship, architecture guidance, compliance oversight, and cross-functional coordination with business, infrastructure, operations, and external partners. Evaluates emerging technologies, manages internal and contracted resources, and ensures data platforms meet business requirements, operational standards, and service-level agreements.

Design and maintain scalable HR data architecture in Snowflake; build and optimize ETL/ELT pipelines with Matillion; enforce data governance, quality, lineage and security; manage SQL Server metadata environments; implement DevOps for data assets; partner with stakeholders and support AI-ready semantics and reusable data products.

Leads a team of data engineers responsible for building and scaling Niche’s data platform. Oversees data pipelines, warehouse layers, platform reliability, data quality, monitoring, and engineering best practices. Manages roadmap execution, Agile rituals, cross-functional collaboration, hiring, mentoring, and career development. The role also drives process improvements, operational excellence, scalability, and delivery of secure, observable data solutions supporting analytics, reporting, content, and operational systems.

Own and maintain the commercial SQL Server database, data pipelines, integrations, master and reference data, validation processes, and external market-data ingestion. Build trusted datasets connecting sales, product, customer, territory, and market information to Power BI reporting. Troubleshoot production incidents, improve pipeline reliability and performance, document data lineage and definitions, and serve as the commercial analytics data expert while partnering with stakeholders and source-system owners.

Oversee data transfer and integration strategies for Electronic Grants System interfaces. Maintain system-to-system data exchanges, develop web services and APIs using JSON, SOAP, and XML, and create advanced queries, data models, and views.

Leads enterprise architecture for data, analytics, and AI/ML platforms. Defines cloud, Lakehouse, streaming, governance, integration, and machine-learning architecture standards; guides modernization, proof of concepts, platform scalability, security, and cost optimization. Partners with executives, product owners, cybersecurity, governance, and engineering teams while leading design reviews, architecture forums, and technical programs. Requires deep experience with Databricks, cloud-native data platforms, distributed systems, data governance, and enterprise-scale architecture.

Designs and governs enterprise-scale data and AI architectures using Databricks and Lakehouse technologies. Responsibilities include Medallion Architecture, semantic layers, data modeling, SAP ECC to S/4HANA integration, ETL/ELT, batch and streaming ingestion, governance, lineage, quality, security, and platform modernization. The role partners with engineering, analytics, AI/ML, SAP, security, and business teams while providing technical leadership and mentorship.

Leads customer data engineering strategy, delivery, team development, and roadmapping. Oversees data-driven product improvements, digital programs, quality initiatives, budgets, cybersecurity remediation, and portfolio management. Manages, mentors, and grows data engineering teams, including contractor teams, while guiding customer data platforms, omnichannel identity resolution, complex data modeling, and personalization-focused data science capabilities. Partners across the organization to execute strategic initiatives and build team capability.

Own the company-wide BI strategy, infrastructure, governance, dashboards, LookML models, forecasting, and analytics request process. Partner with executives, business owners, Data Engineering, Product, and Engineering to improve data reliability, translate business questions into recommendations, and surface trends and opportunities. The role requires SQL, BI tooling, and data warehouse expertise, with Python, predictive modeling, and statistical analysis preferred.

Leads the Data Engineering and Analytics team, managing people, technical strategy, intake prioritization, data pipelines, governed reporting, and stakeholder delivery. Oversees Python, SQL, dbt, Airflow, and BigQuery data products while guiding predictive analytics and machine-learning initiatives such as forecasting, recommendations, and segmentation. Establishes quality, reliability, governance, and development standards; mentors team members; supports debugging, process improvement, monthly reporting, invoices, and scorecards. The role also promotes responsible AI adoption and PHI-compliant data practices.

The Data Analytics Manager partners with business stakeholders to translate reporting needs into data solutions. Responsibilities include building Power BI or similar dashboards, managing data quality and governance, defining metrics, analyzing trends, prioritizing analytics requests, collaborating on data infrastructure, training users, and mentoring analysts. The role requires strong SQL, data modeling, ETL/ELT, data warehousing, stakeholder management, and communication skills.

Manages people and technical data projects while developing quantitative analyses, visualizations, ETL processes, databases, data pipelines, and cloud-based solutions. Works with complex geospatial data and creates data-driven insights. Preferred experience includes Agile/SAFe, NGA enterprise systems, MLOps, GEOINT certification, and AI/ML applications to geospatial data. Requires TS/SCI clearance and a current CI polygraph immediately after starting or within scope.

Manages data teams and technical projects while performing quantitative analysis, visualization, ETL, database and data pipeline management, cloud architecture work, and processing complex geospatial datasets. The role requires strategic problem-solving, people management, and a TS/SCI clearance. Desired experience includes Agile/SAFe, NGA enterprise systems, MLOps, GEOINT certification, and applying AI/ML to geospatial data.

Manages the enterprise ETL practice and data integration architecture. Designs and supports Informatica integrations, data synchronizations, APIs, Oracle SQL procedures, Salesforce workflows, Marketing Cloud data extensions, and Data Cloud components. Collaborates with technical teams and business partners, monitors and optimizes integration operations, troubleshoots issues, improves quality assurance, provides technical guidance and staff training, and contributes to integration policies and plans.

Leads a team of data analysts supporting marketing campaigns and projects. Defines audience, segmentation, and measurement requirements; designs Alteryx workflows and ETL processes; connects Salesforce, Data Cloud, Databricks, and other platforms; ensures data quality, integrity, privacy, and regulatory compliance; and improves reusable data delivery processes across marketing technology systems.

Leads the Data Architecture, Tools, and Metrics team within Quality and Mission Assurance. Develops data strategy, drives analytics adoption, monitors operational data, and delivers recommendations. Designs scalable data pipelines, ETL processes, analytics data models, and data quality frameworks. Partners with stakeholders and sector leaders to solve operational issues, presents complex analytics, and leads cross-functional engineering teams delivering customer-driven products.

Designs, builds, tests, and supports enterprise data pipelines and Snowflake archival solutions for acquisitions, integrations, reporting, compliance, and analytics. Translates business and technical requirements into mappings, transformations, quality rules, and deployment plans. Collaborates with architecture, security, infrastructure, application, business, and vendor teams. Oversees validation, monitoring, documentation, cutover, go-live readiness, hypercare, and operational handoff while improving reusable, secure, cost-efficient data engineering practices.

Leads data governance for acquired-company integrations, embedding ownership, stewardship, quality, metadata, lineage, retention, privacy, legal holds, and compliant data use across structured and unstructured content. Establishes data inventories, quality rules, retention and disposal processes, governance controls, and readiness metrics. Partners with business, technology, legal, privacy, risk, security, and implementation teams, while producing executive reporting and supporting governance technology adoption.

Lead development and delivery of analytical datasets and AI-enabled analytics for global real estate. Partner with stakeholders to translate business needs, design data solutions, enforce governance and quality, scale AI use cases, and deliver executive-level reporting and project management.

Leads a data engineering team responsible for enterprise data platforms, lakehouse architecture, production pipelines, governance, security, reliability, and operational excellence. Combines people leadership with hands-on technical guidance across AWS, Snowflake, SQL, Python, data quality, observability, and compliance. Partners with scientific, clinical, product, analytics, security, and IT teams to deliver scalable, trusted data capabilities while managing priorities, technical debt, vendors, and team development.

Lead the Managing Data Steward team for Small Business Bank to define and execute data management strategy, governance, quality controls, and data product development. Drive adoption of open-source and cloud-native (AWS) technologies, oversee data catalog health, monitor KRI's, manage data issue resolution, and report insights using advanced visualization. Coach and develop a high-performing analytics team while partnering with engineering and enterprise functions to standardize data flows and migrate on-prem systems to cloud.

The role involves leading a team to develop AI and ML solutions, manage customer interactions, and analyze large data sets using various technologies.

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.

Manages research data acquisition, licensing, vendor relationships, contracts, data use agreements, governance, and compliance. Leads data safety reviewers, assesses privacy and contractual risks, develops SOPs and access workflows, trains researchers, and coordinates with faculty, vendors, legal counsel, procurement, compliance offices, and university stakeholders.

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