Top Senior Data Management Jobs in Boston, MA

Lead a team delivering actuarial statistical reporting, regulatory submissions, and analytics. Ensure compliance with bureau requirements, drive automated reporting and UAT for policy/claims systems (Guidewire, Solartis), partner with actuarial, technology, and vendors, and manage stakeholder/regulator interactions while mentoring staff and improving data governance and reporting processes.

Lead and develop a team of data engineers while contributing hands-on to design, implement, and operate scalable, secure data pipelines. Partner with product, software, and architecture teams to deliver cloud-native data solutions, ensure production reliability, and drive engineering best practices and continuous improvement.

Manages reporting and analytics operations, teams, workflows, critical business intelligence reports, and performance metrics. Leads data process improvements, requirements analysis, systems development, testing, reporting projects, and process re-engineering. Uses SQL, data warehouses, QlikView, Tableau, and related tools to support operational, financial, quality, compliance, and strategic decisions. Mentors staff, manages priorities, monitors risks, and partners with business leaders and clients.

Leads the strategy, architecture, and execution of an enterprise data platform supporting analytics, machine learning, operational systems, and AI applications. Manages and mentors data platform engineers while remaining hands-on with architecture, reliability, technical direction, and troubleshooting. Oversees pipelines, orchestration, governance, security, observability, data contracts, migrations, and platform operations. Partners cross-functionally to translate business needs into scalable technical solutions and communicates decisions to technical and non-technical stakeholders.

Lead development and maintenance of equity analytic datasets, pipelines, and factor models to support quantitative research, portfolio risk and return attribution. Ensure data quality and process stability, deliver recurring and ad-hoc analytics, build dashboards, troubleshoot production issues, and provide technical and strategic guidance while mentoring junior staff and coordinating across business, technology, and vendor teams.

Lead enterprise data strategy and analytics initiatives: translate requirements into data products, validate data quality, build dashboards and reports, develop cloud-based analytics using AWS and BigQuery, implement digital analytics with Adobe Analytics, mentor analysts, and deliver actionable insights using SQL, Python, R, and BI tools.

Manage biographical data integrity for AA&D CRM (Advance): oversee scheduled data projects (graduation conversions, NCOA, voter files), design batch imports/exports, run data cleaning and QA, build queries/reports with SQL and BI partners, test changes, supervise one direct report, and collaborate across teams to improve processes and leverage AI/RPA for efficiency and accuracy.

Manage a team delivering accurate, timely data reporting for Transfer Agency clients. Build advanced Excel data models (Power Pivot, DAX, Power Query), automate reporting, develop dashboards and KPIs, ensure data quality and governance, and support process improvement and stakeholder reporting needs.

Owns the end-to-end architecture, development, infrastructure, data pipelines, and Power BI reporting platform supporting data governance, informatics, analytics, auditing, and data quality. Leads and mentors a data engineering team, defines schemas and engineering standards, ensures security and HIPAA compliance, oversees testing and Agile delivery, manages stakeholder requirements, and remains hands-on with backend technologies, data models, Power BI, and DAX.

Manage CVS Health's data partnerships and monetization strategy, source new partnerships, ensure operational success, and drive data product adoption across analytics teams.

Lead a hands-on data engineering team to build and scale production data pipelines, datasets, and observability for alternative-data products. Drive architecture, data modeling, QA, and AI-ready dataset design while coaching engineers, partnering with product and research, and owning operational excellence across Databricks, Airflow, SQL, and PySpark.

Serve as the analytics bridge between marketing/operations and data teams: translate business questions and KPIs into clear product requirements, own delivery of treated tables, semantic layers, reports and dashboards, align stakeholders on a single source of truth, run gap analyses, document processes, and ensure delivered data products drive decision-making and adoption.

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.

Lead delivery, support, and continuous improvement of enterprise reporting, dashboards, and analytics using Power BI and Microsoft Fabric. Partner with executives and business process leaders to define KPIs, enforce reporting governance and data quality, mentor analysts, manage cross-functional analytics projects, and drive analytics adoption and operational transparency.

Lead a remote US team that builds and operates ETL and migration processes to move legacy EHR data into athenahealth. Oversee Python-based tooling, define source-to-target mappings, ensure HL7/FHIR interoperability and EHI export compliance, drive data quality, automation, documentation, stakeholder alignment, and coach engineers.

Manage client-facing data and analytics projects: define data strategy, build automation and ETL, develop visualizations and predictive analytics, drive transformation, manage scope/budget, and communicate findings to non-technical stakeholders while leveraging finance knowledge.

Manage and grow a data engineering team, overseeing the design and maintenance of data platforms using Snowflake, dbt, and Azure. Drive best practices in data management and ensure reliable data movement and quality.

The role involves leading the data governance team, managing IT projects, collaborating with stakeholders, and ensuring continuous improvement of processes. Responsibilities include strategic planning, client management, and optimizing resource utilization, while overseeing governance activities and departmental management.

The Data Product Manager defines the reporting roadmap, manages KPI alignment, collaborates cross-functionally, ensures data quality, and leads continuous improvement in analytics processes.

Lead design, build, and operation of data architecture, pipelines, and semantic layers. Manage sprint planning, DevOps metrics, data quality and monitoring, and MLOps enablement. Partner with stakeholders, coach engineers, track project plans, and ensure data availability, accuracy, and documentation.

Lead and grow a data engineering team; define architecture and standards for ETL/ELT pipelines; hands-on design, code review, and development; ensure production reliability, monitoring, and incident response; partner with product and engineering on data roadmap.

Manage receipt-based data ingestion (OCR, transcription, email, account connections); ensure pipeline stability and data quality; translate business requirements into collection specs; partner with Engineering and Product; manage vendors, forecasting and budgets; produce KPI reporting; and support EU rollout and GDPR-aligned processes.

Lead and scale the enterprise data hub: manage the data warehouse, build reliable ingestion and transformation pipelines, implement governance and data modeling best practices, deliver GTM-ready metrics, enable AI/ML use cases, prioritize the data roadmap, and lead/mentor a data engineering team.

Lead combined Analytics Engineering and Data Engineering teams owning the end-to-end data roadmap from ingestion to trusted metrics. Manage hiring and performance, set architecture and platform investments, enforce testing/monitoring/documentation standards, review technical decisions, and partner with product and business stakeholders to translate needs into prioritized deliverables.

Lead and grow a data engineering team to design, build, and operate scalable, cost-efficient data lakehouse pipelines and infrastructure. Define technical strategy, enforce engineering best practices (CI/CD, testing, monitoring), own roadmap and capacity planning, collaborate with product and engineering stakeholders, manage on-call and operational automation, and hire talent. Ensure data governance, lineage, and compliance in a regulated healthcare environment.