Top Data Management Jobs in Philadelphia, PA

Lead design and implementation of data architecture for healthcare systems, influence stakeholders, mentor teams, ensure regulatory compliance, enable data integration and transformation, and apply AI techniques to improve healthcare operations and reporting.

Lead data validation and risk initiatives using advanced analytics, statistical modeling, and visualization to extract insights, improve data quality, build pipelines, and deploy scalable data solutions. Manage client accounts, mentor teams, and collaborate with stakeholders to address data challenges and optimize business performance.

Lead development and execution of data-driven strategies, manage project planning and delivery, mentor and coach team members, ensure quality and ethical standards, design cloud data architectures and governance frameworks, analyze complex datasets to produce actionable insights, and drive continuous improvement using advanced analytics and modern data technologies.

Lead development and implementation of data governance strategies, manage client accounts and deliverables, supervise and mentor teams, analyze data to inform business decisions, ensure compliance with firm standards, and identify improvements in data management processes.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Leads ad hoc and exploratory analyses for medical and business questions across a global Medical Insights organization. Prepares and validates datasets, translates ambiguous stakeholder needs into decision-ready findings, and maintains analytical documentation. Tests new data sources, methods, and AI-enabled approaches through proof-of-concept analyses to guide platform investments and roadmap decisions. Partners with business and product stakeholders while supporting compliant, reproducible use of regulated data.

Leads the enterprise data platform strategy, architecture, governance, and evolution. Manages a central data team and enables federated self-service analytics across business functions. Builds AI-ready data pipelines, RAG and Graph RAG architectures, custom data applications, observability, cost optimization, and AIOps capabilities. Requires hands-on expertise with Snowflake, dbt, modern ETL, Python, SQL, CI/CD, and Data Mesh architectures, along with 5+ years leading technical data teams.

Develops credit card analytics strategies to grow partner relationships and customer engagement. Integrates and analyzes large data sets, applies statistical techniques and predictive modeling, builds secure data environments, evaluates measurement tools, and delivers actionable insights. Leads dashboard and performance metric development while collaborating with marketing, product, analytics, vendors, and other cross-functional teams.

Leads and develops a Data Platform engineering team while owning delivery of complex data initiatives. The role establishes delivery discipline, assesses technical and implementation risks, partners cross-functionally, and drives production reliability, data quality, observability, incident management, and on-call readiness. It also guides the evolution of scalable data ingestion, transformation, lakehouse, cloud architecture, governance, and reusable platform capabilities.

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

Leads data governance for acquired-company integrations, embedding governance, ownership, quality, metadata, lineage, retention, privacy, legal hold, and compliance requirements across structured and unstructured data. Establishes data inventories, stewardship models, quality controls, retention schedules, and approval processes; coordinates with business, technology, legal, risk, privacy, and security stakeholders. Supports migration, archival, modernization, analytics enablement, governance tooling, executive reporting, and cross-functional transformation readiness.

Designs, builds, tests, and supports enterprise data pipelines for acquired-company system integration and Snowflake archival. Translates business and technical requirements into mappings, transformations, quality controls, monitoring, and deployment plans. Collaborates with architecture, security, infrastructure, application, business, and vendor teams to deliver governed, reusable data solutions. Supports migration, cutover, validation, documentation, hypercare, and operational handoff while improving automation, performance, cost efficiency, and data observability.

Leads a team of five experienced analysts and data scientists while personally owning strategic analytics across ecommerce, health, supply chain, inventory, fulfillment, and merchandising. Partners with executives, establishes KPI and reporting standards, prioritizes the analytics roadmap, and delivers actionable business insights. The role also advances AI-native, self-service analytics capabilities, strengthens data governance and quality, and builds a high-performing analytics culture.

Leads enterprise data quality strategy and operations across Johnson & Johnson’s One Data Platform. Responsibilities include defining quality standards and controls, profiling and validating commercial pharmaceutical data, monitoring dashboards and metrics, coordinating root-cause analysis, driving remediation, and improving data readiness for analytics and AI. The manager partners with business, engineering, governance, master data, and external provider teams while leading delivery partners and coaching team members.

Leads a data engineering team and oversees the design, development, reliability, security, and scalability of data pipelines, warehouses, platforms, and integrations. Translates business needs into technical solutions, establishes data quality and governance standards, reviews architecture and code, manages projects and resources, resolves infrastructure issues, mentors engineers, and communicates progress and risks to leadership.

Manage Finance-focused data products, collaborating with teams to define requirements and drive delivery for financial analysis and reporting.

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.

Leads data engineering and data science teams, setting strategy and delivering scalable pipelines, data architecture, models, analytics infrastructure, and data governance. Partners with product, analytics, and business stakeholders to prioritize initiatives, assess upstream changes, maintain data quality, and support Snowflake-based warehousing. This hands-on leadership role includes mentoring staff, contributing to technical codebases, and advancing data science and engineering solutions.

Leads a team of business and data analysts while owning the analytics roadmap for commercial, sales, and go-to-market functions. Translates business needs into analytical frameworks, KPIs, dashboards, models, and reporting strategies. Partners with senior stakeholders and cross-functional teams to drive data-informed decisions, governance, quality, and best practices. Provides mentorship, oversees solutions using modern data tools, and presents recommendations supporting planning, forecasting, and execution.

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.

Owns governance, quality, standards, and lifecycle management for eight master data domains across ERP and related systems. Administers NetSuite master data, defines validation and error-proofing controls, resolves data discrepancies, maintains audit-ready documentation, and reports quality KPIs. Partners with IT, Supply Chain, Finance, Commercial, Regulatory, Customer Care, and other teams on integrations, migrations, product launches, compliance, serialization, and pricing. Provides cross-functional guidance and drives data discipline without direct authority.

Manages client, compliance, and performance reporting across healthcare programs using eligibility and claims data. Leads reporting analysts while remaining hands-on with advanced SQL, dashboards, data crosswalks, validation, reconciliation, root-cause analysis, and data-quality monitoring. Partners with data engineering and clients to resolve upstream issues, establish reporting standards, and guide the analytics roadmap.

Manages a data engineering team responsible for scalable, reliable data pipelines, infrastructure, and analytics tooling. Provides mentorship, performance management, and technical leadership while coordinating with product, analytics, finance, compliance, engineering, and regulators. Prioritizes cross-functional projects, supports data architecture and governance, ensures performance and reliability, and operates within strict regulatory and audit requirements.

Analyzes, cleanses, validates, and prepares operational datasets for mailing, email, reporting, billing, and processing activities. Develops Databricks queries and Alteryx workflows to automate processes and improve data quality. Performs audits, reconciliations, root-cause analysis, and corrective actions while maintaining governance and security compliance. Provides actionable insights, mentors junior analysts, communicates findings, and leads continuous improvement initiatives.

Lead and mentor a data engineering team building scalable customer-focused data solutions. Hands-on technical ownership, architecture guidance, and delivery of data ingestion, transformation, and reporting. Advance AI-enabled development practices, set technical standards, and foster a collaborative, accountable engineering culture to improve speed, quality, and reliability.