Top Senior Level Data & Analytics Jobs in Boston, MA

Leads client-facing data validation and risk workstreams, developing reliable data pipelines, integrations, transformations, and validation controls. Manages project plans, budgets, timelines, deliverables, and team performance while analyzing data structures and system interactions to identify risks. Coaches team members, collaborates with stakeholders, improves database and query performance, promotes automation and AI-enabled tools, and ensures technical standards, documentation, and independence requirements are maintained.

Designs reusable data ingestion and transformation components, automates data quality, governance, testing, lineage, and deployment workflows, and improves pipeline observability and reliability. The role supports monitoring, incident response, cloud and container integrations, infrastructure as code, documentation, reusable datasets, and Agile platform improvements. Candidates need five or more years of data engineering experience plus expertise in orchestration, distributed processing, storage, scripting, automation, and version control.

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.

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.

Leads commercial analytics across digital commerce, apparel and accessories, and wholesale. Manages analysts, owns bookings forecasting, develops customer and channel economics frameworks, and builds scenario-planning and measurement models. Partners with Finance and senior stakeholders to evaluate growth opportunities, diagnose performance, quantify tradeoffs, and guide investment decisions. Requires strong SQL, analytical modeling, people leadership, and cross-functional business judgment.

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.

Own the design, governance, and evolution of the enterprise data lake and warehouse. Responsibilities include developing conceptual, logical, and physical data models; defining source-to-target mappings; reconciling legacy CRM and operational data; establishing data quality frameworks; administering schemas, views, access controls, and performance; and partnering with engineering and business stakeholders to support scalable, self-service analytics.