Top Data & Analytics Jobs in Boston, MA

Design and build data validation, integration, and transformation solutions to support ERP implementations and risk/regulatory clients. Develop data pipelines and infrastructure, manage data validation processes, ensure data lineage and quality, manipulate large datasets using Excel, Alteryx, SQL, Python, Databricks, or Snowflake, and collaborate with clients to deliver scalable, governed data solutions.

Lead the strategy and execution of real-world data capabilities, focusing on data lifecycle management, vendor relationships, and collaboration with various teams to deliver analysis-ready datasets.

The Senior Data Engineer will transform real-world data into analytics-ready datasets, build scalable pipelines, and conduct analyses to support scientific and business decisions, collaborating closely with data science teams.

Build and productize externally consumable data assets from concept to delivery. Define schema, governance, SLAs, documentation, and delivery (Snowflake share, SFTP, API). Partner cross-functionally to productionalize datasets, monitor quality, iterate on customer feedback, and create repeatable productization frameworks.

Partner with Sales, Product, Engineering, and Operations to build the GTM analytics foundation for a new dealer software pillar. Own pipeline, install base, churn, demo-to-close, and launch analytics; identify and fix Salesforce/process gaps; create prospecting and churn-risk signals; write production-grade SQL on Snowflake and productionize assets in dbt; and maintain dashboards in Looker, Omni, and Salesforce CRM Analytics to drive measurable go-to-market execution.

Lead Data Scientist responsible for guiding projects, mentoring a team, developing data-driven solutions, and collaborating across departments to drive insights.

The VP of Alternative Data Strategy drives sourcing, prioritisation, and implementation of alternative datasets, aligning data opportunities with investment strategies and governing data usage across teams.

The Senior Data Analyst will provide data analysis to drive strategic decisions, utilizing SQL and Python for insights and data visualization, engaging with cross-functional teams, and developing interactive dashboards.

Lead RevOps data modeling, ETL/DBT development on Snowflake, SQL-based analytics, and Tableau reporting. Optimize and maintain RevOps systems (Salesforce, Clari, Outreach, Gong/Chorus), ensure data quality, apply advanced analytics/AI concepts for forecasting, and partner cross-functionally to deliver actionable revenue insights.

Lead RevOps data modeling, ETL/DBT development, and dashboarding to ensure high-quality revenue data. Build Snowflake data marts, author complex SQL, create Tableau visualizations, apply AI/ML forecasting, and partner with Sales, Marketing, Finance, and IT to resolve data quality and reporting issues.

Partner with Growth Marketing to analyze campaign performance, quantify opportunities, build dashboards, and communicate data-driven recommendations. Own analytics for reach and partnership channels (including Audio and Influencer), apply A/B testing and experimental design, and explore LLM-powered tools to improve workflows and decision-making.

Lead and scale a casino analytics team to drive data-informed strategy, experimentation, and reporting. Partner with product, data science, and operations to deliver executive insights, build analytical frameworks, and develop high-performing analysts to unlock growth and operational efficiency.

Lead design and implementation of enterprise data governance: define standards, operating model and AI-ready data criteria; partner with data product managers, engineering, security, legal and privacy to operationalize metadata, lineage, access controls, quality monitoring, and self-service for analytics and AI.

Lead strategy, architecture, and execution of an enterprise data engineering platform. Build and manage a high-performing team, modernize cloud-native data platforms, establish engineering standards (ingestion, transformation, orchestration, governance, observability), enable analytics and AI/ML, drive DataOps and CI/CD, manage vendors and budgets, and present roadmap and risks to executive leadership.