Top Senior Data & Analytics Jobs in Boston, MA

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

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 a team of analytics professionals and owns client and product analytics strategy, dashboards, reporting, metrics governance, and self-service capabilities. Partners with Product, Engineering, Client Success, and account teams to define trustworthy KPIs, improve data quality, translate complex data into actionable insights, and prioritize analytics initiatives. The role combines people leadership with hands-on BI solution design and supports client-facing analytics offerings.

Lead Data Engineer developing cloud-based data and AI solutions for risk management. Responsibilities include building, testing, deploying, and supporting full-stack systems; developing data pipelines, distributed and streaming applications; using cloud warehouses, RDBMS, NoSQL databases, and machine learning technologies; conducting code reviews and unit tests; collaborating across Agile teams; and mentoring engineers. The role requires application development, big data, and cloud experience, with preferred expertise in real-time data, data warehousing, distributed computing, and AI tooling.

Design and execute complex analyses evaluating property claims operations, process changes, tools, and vendor partnerships. Build reporting frameworks, synthesize multi-source data, and translate findings into actionable recommendations for senior stakeholders. Independently lead analytical projects that improve financial outcomes, claims handling, customer results, and operational efficiency.

Conduct quantitative and qualitative research, including data cleaning, statistical analysis, subgroup comparisons, regression, clustering, and significance testing. Manage online research communities for teens, parents, and counselors, overseeing recruitment, engagement, incentives, research programming, and member experience. Translate findings into dashboards, visualizations, reports, and presentations for nontechnical audiences. Collaborate cross-functionally to shape research agendas, deliver rapid insights, and advance data-driven initiatives.

Own and improve e-commerce fulfillment analytics capabilities, including solution design, coding, deployment, data pipelines, validation, modeling, reporting, and visualization. Analyze internal and external data to generate insights for network strategy, capacity, investment, sales capture, and cost management. Partner with data, fulfillment, brand, and executive teams while managing roadmaps, vendors, documentation, testing, and continuous improvement.

Own end-to-end data products, including source onboarding, API and file-feed ingestion, orchestration, incremental and idempotent pipelines, Snowflake and dbt Cloud modeling, testing, documentation, alerting, and production reliability. Establish engineering standards, manage secrets and governance, use AI coding tools responsibly, collaborate with vendors and stakeholders, and mentor junior engineers.

Leads the Managed Services analytics roadmap, KPI governance, Power BI dashboard strategy, reporting automation, and AI-augmented metrics services on InterSystems IRIS. Provides technical leadership in SQL, ObjectScript, APIs, data models, interoperability, query optimization, and application performance. Establishes data-quality and reporting standards, develops predictive analytics, mentors analysts and engineers, investigates complex issues, and advises senior leadership on operational performance, risks, and investment priorities.

Designs, builds, administers, and supports cloud data architectures, databases, schemas, metadata repositories, ETL processes, and analytic models. Leads data quality, governance, troubleshooting, testing, automation, reporting, dashboards, and infrastructure design. Collaborates with Agile teams to translate business requirements into technical capabilities and supports decision-making for Audit and business management. The role is full-time onsite at an eligible Federal Reserve location and requires extensive security screening.

Leads the data engineering team, including hiring, coaching, technical direction, platform architecture, data pipelines, Snowflake warehousing, governance, quality, observability, and incident support. Partners with Data Science, Analytics, Clinical Operations, Biostatistics, and Business Development to deliver trusted data products for clinical operations, asset evaluation, reporting, and machine learning. Oversees budgeting, resourcing, documentation, technical programs, and responsible use of AI engineering tools.