Top Data Management Jobs in Boston, MA

Design, build, test, and support scalable cloud data solutions and pipelines. Develop data platforms using Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases, and cloud technologies. Establish reusable data engineering patterns, security controls, and operational standards. Collaborate with product managers, engineers, analysts, and data scientists; communicate technical outcomes; mentor data professionals; and deliver resilient, high-performance financial data products.

Leads Customer Success analytics strategy, executive business reviews, customer experience and voice-of-customer insights, enablement impact measurement, and data foundation development. Manages two analysts, partners with data engineering and data science, applies statistical and experimental methods, analyzes structured and unstructured customer data, and uses AI tooling to improve self-service and recurring analytics. Translates ambiguous business questions into actionable recommendations for senior leaders.

Leads Motorsports Data Infrastructure and Data Science teams while owning the strategy, architecture, and delivery of data platforms supporting telemetry, simulation, trackside, analytics, and AI/ML use cases. Responsibilities include managing managers, designing streaming and batch pipelines, productionizing ML models, setting engineering standards, managing Azure and Databricks capacity, protecting sensitive data, and partnering with racing and executive stakeholders. The role remains hands-on in architecture, coding, code review, incident support, and technical problem-solving.

Manage the development, implementation, governance, and adoption of enterprise ontologies and optimized data models. Partner with Finance, Technology, Product, Machine Learning, and enterprise teams to support ERP replacement, semantic technology integration, data standardization, and data harmonization. Own the ontology roadmap, establish standards using W3C technologies, communicate strategic vision, manage and mentor teams, and advocate for semantic technologies across the organization.

Build and architect scalable cloud data platforms, pipelines, and applications. Design distributed, real-time, and batch data solutions using Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases. Establish reusable engineering patterns, data security controls, and operational standards. Collaborate with product managers, software engineers, analysts, and data scientists while mentoring team members and communicating technical outcomes to stakeholders.

Designs conceptual, logical, and physical data models for R&D research and clinical domains. Translates scientific and business requirements into reusable, scalable, AI-ready data structures that support analytics, machine learning, data integration, and standardized workflows. Partners with domain experts, data engineers, architects, data scientists, and governance teams to apply enterprise standards, schemas, ontologies, metadata, quality rules, and lineage. Promotes shared data assets and ensures models are implementable, interoperable, and aligned with evolving R&D needs.

Leads data engineering architecture and modeling initiatives, designing scalable pipelines, cloud data platforms, and reusable data solutions. Collaborates with engineering, product, analytics, and data science teams while mentoring developers and establishing common design patterns. Responsibilities include distributed data processing, relational and NoSQL systems, data security, observability, streaming pipelines, and operational excellence across platforms such as Databricks and Snowflake.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Develop distributed data solutions using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Establish reusable engineering patterns, data security controls, and operational standards. Collaborate with product, engineering, analytics, and data science teams while mentoring others and communicating technical outcomes to stakeholders. Support resilient, high-performance data workloads and contribute to Agile delivery.

Leads enterprise data management, governance, quality, lifecycle, metadata, stewardship, database, and repository activities. Develops data policies and standards, resolves complex data issues, oversees migration and integration efforts, ensures compliance and access controls, and improves data reliability across systems. Partners with executives, technical teams, analysts, cybersecurity, and compliance stakeholders while providing leadership to staff and preparing senior-management briefings. Requires expertise in SAS, SQL, AWS, and data-intensive domains, with medical or Military Health System data experience preferred.

Leads enterprise data management, governance, quality, lifecycle, metadata, stewardship, database, and repository activities. Develops policies and standards, oversees data integrity and access, resolves complex data issues, supports migrations and integrations, and ensures compliance with security and contractual requirements. Partners with executives, technical teams, analysts, and stakeholders while providing leadership to data staff. Uses SAS, SQL, and AWS to manage and optimize large-scale data, preferably including Military Health System records.

Lead and grow a global data engineering team while establishing foundational data platforms and engineering standards. Design and oversee GCP-based pipelines, medallion data models, DataOps, monitoring, SLAs, CI/CD, governance, and data quality practices. Drive AI adoption across engineering workflows and build reliable, AI-ready data. Partner with business and technology stakeholders to prioritize roadmaps, deliver scalable solutions, and support data democratization.

Owns the Source-to-Pay data and analytics foundation, including master data, taxonomy, governance, quality, analytics products, automation, and AI readiness. Leads business requirements, operating models, executive dashboards, KPI frameworks, decision-support insights, and cross-functional platform enablement. Partners with Procurement, Finance, Legal, Risk, IT, and enterprise data teams to improve data reliability, controls, reporting, self-service analytics, and scalable automation.

Generate actionable customer and product insights by synthesizing large, complex datasets, developing hypotheses, conducting experimentation and analysis, and translating findings into strategic business decisions. Collaborate cross-functionally with analytics, engineering, data science, marketing, and product teams. Build dashboards and reports, identify trends and growth opportunities, communicate customer experience insights, and strengthen analytics capabilities across the organization.

Lead and coach teams to design, code, test, and deliver software solutions driving digital transformation for tax clients. Translate requirements into technical specs, perform code reviews, troubleshoot complex issues, maintain client relationships, and promote innovation and systems thinking to validate outcomes and improve products.

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 deployment of data-driven software solutions within the Tax practice. Manage client relationships, guide teams in design/coding/testing, enforce coding standards, implement CI/CD, integrate enterprise applications, conduct code reviews, mentor staff, and ensure projects are planned, budgeted, executed, and completed to meet client expectations.

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.

Own and operate Toast’s enterprise data governance program, including data domain ownership, governance council coordination, policy compliance, business glossary management, metadata standards, catalog adoption, maturity assessments, and data literacy training. The role partners with business stakeholders, data stewards, catalog teams, and leadership to improve data trust, compliance, discoverability, and quality. It requires governance, metadata, SQL, data catalog, program management, and workshop facilitation experience.

Leads the development and operationalization of governed clinical and multi-omics data products for neuroscience research. Designs scalable data models, optimizes Nextflow pipelines across AWS S3, Snowflake, and TileDB, standardizes assay outputs, embeds FAIR principles and compliance, and delivers AI-ready datasets. Partners with architects, governance teams, AI/ML specialists, and data scientists while mentoring engineers and driving workflow standardization, quality, reproducibility, and pipeline optimization.

Manages data and records for a Department of the Air Force battle-network division, including metadata, knowledge management, CDRLs, configuration and change control, audits, databases, data standards, cybersecurity compliance, and SharePoint administration. Produces reports, tracking tools, file plans, and analytical findings while supporting acquisition documentation, records management, database performance, backup and recovery, and SharePoint users.

Leads LTK’s Data Engineering team, owning technical direction, architecture, pipelines, data products, governance, quality, reliability, and platform modernization. Manages and develops engineers, drives roadmaps and complex initiatives, partners with cross-functional stakeholders, and balances scalability, security, performance, cost, and business priorities. Establishes engineering standards, improves operational practices, manages risks and dependencies, and builds trusted, reusable data capabilities supporting analytics, reporting, products, and AI use cases.

Leads a team of 5–8 data engineers and analysts while remaining hands-on with Databricks and Azure data platforms. Owns scalable pipelines, Lakehouse architecture, data products, quality, governance, reliability, security, and cost management. Oversees hiring, coaching, performance, delivery prioritization, agile execution, production health, incident response, stakeholder communication, and cross-team collaboration. Helps shape global data engineering strategy, standards, technology adoption, and platform evolution.

Leads Fetch’s centralized Operations Analytics team, establishing standards, operating models, KPI definitions, dashboards, and reporting practices. Manages and develops Operations Data Analysts, partners with senior operational leaders, and delivers insights supporting strategy, staffing, process improvement, and decision-making. Oversees reporting in Hex, Tableau, and Grafana; improves data quality and automation with engineering; and translates ambiguous operational data into measurable recommendations and business outcomes.

Manages DoD data, records, configuration, and change-management processes. Maintains CDRLs, equipment inventories, records plans, databases, and SharePoint environments; supports audits, data requirements, reporting, compliance, and database administration. Collaborates with government staff and contractors, provides user support, develops management briefings, and ensures cybersecurity and information-security practices are followed.

Owns and maintains organizing data systems, voter-contact tools, and organizational databases. Ensures data integrity through quality assurance, audits, standardized processes, documentation, and permission management. Supports organizers by troubleshooting tools, managing data requests, providing training, and improving workflows. Writes SQL queries in BigQuery, develops reports and visualizations, supports integrations, and collaborates with engineering and analytics teams on data models and priorities.