Top Senior Level Data & Analytics Jobs in Boston, MA

Performs data analysis, cleans and validates data, identifies trends and anomalies, builds dashboards and reports, and supports KPI development. Collaborates with engineers and business stakeholders to improve data quality, pipelines, reporting requirements, and analytical processes. Leads data-driven change management, communicates insights, shares best practices, and may support urgent IT incidents.

Manage client data, analytics, and AI managed-services workstreams. Develop data pipelines, dashboards, reporting, and analytics outputs; remediate data-quality issues; tune AI models and platform components; and improve data operations, observability, and reporting workflows. Collaborate with clients to translate business needs into data solutions, interpret insights, review deliverables, and mentor junior team members.

Lead analytical investigations and build measurement frameworks, dashboards, reporting systems, and reusable Snowflake-backed data products for Customer Experience teams. Partner with data scientists on health scoring, forecasting, segmentation, and validation. Translate ambiguous business questions into decision-ready insights, communicate findings to senior stakeholders, and document solutions for operational handoff.

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.

Own high-impact revenue strategy analyses from problem framing through recommendations and decision support. Analyze marketplace performance, lead economics, monetization, retention, product adoption, and operating models using SQL, Python, financial modeling, and cross-functional data. Advise senior leaders, create executive- and board-ready materials, improve metric reliability, build reusable analytical frameworks, and influence decisions across Revenue, Product, Finance, and Operations.

Designs, builds, and maintains secure data pipelines, ETL processes, data warehouses, and quality controls. Collects, validates, cleanses, and analyzes data; develops SQL queries, Tableau dashboards, and AWS-based solutions; investigates data integrity issues; documents standards; and partners with stakeholders to deliver technical solutions using Agile and DevOps practices.

Own commercial analytics for direct-to-consumer and marketplace channels, analyzing pricing, promotions, conversion funnels, merchandising, product mix, customer behavior, and channel economics. Build scalable reporting tools, support forecasting and planning, and translate transactional and behavioral data into recommendations for digital commerce leadership. Partner with Marketing Analytics and Analytics Engineering to connect acquisition trends with commercial outcomes and ensure trusted data.

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.

Build and operate reliable data systems and products supporting clinical operations, drug evaluation, business development, analytics, machine learning, and AI agents. Responsibilities include designing pipelines, canonical data models, data contracts, warehouse transformations, quality and observability practices, governance for regulated data, and incident response. The role partners closely with Product Engineering and Data Science, uses AI-assisted development, contributes to architecture, and mentors other engineers.

Build and operate scalable healthcare data pipelines, dbt models, event-driven services, APIs, and Google Cloud infrastructure. Model and govern multimodal EHR, genomic, and imaging data for AI agents across federated hospital networks. Ensure data quality, lineage, observability, security, HIPAA compliance, and production reliability while contributing to TypeScript application services and agentic workflows.