Top Data & Analytics Jobs in Boston, MA

Manage the data architecture and strategic planning for the Sales team, optimizing processes through data and AI for targeting accounts and pricing strategies.

The Sales Revenue Analyst will manage commission processes, analyze compensation results, and partner with various departments to optimize sales performance.

This role focuses on post-sale technical execution, ensuring customer onboarding and deployment, driving customer consumption of products, writing production code, diagnosing issues, and expanding adoption across teams.

Lead enterprise healthcare data modeling: design and maintain conceptual/logical/physical models, integrate varied clinical sources, implement common data model transformations, monitor data quality, and collaborate with clinicians, data scientists, and engineers.

Design, build, and maintain high-throughput data processing services, focusing on streaming and batch data systems to enhance player experiences.

The Analytics Engineer will build and optimize data transformation models, assist with BI tools, support stakeholders, document processes, and implement AI tools, contributing to a data platform under mentorship.

Lead a team to design and operate scalable Databricks/lakehouse platforms, ETL/ELT pipelines, data models, BI dashboards, and productionized ML. Ensure data quality, governance, and deliver actionable analytics and KPIs to drive business decisions while mentoring engineers, scientists, and analysts.

The Associate Principal will translate client needs into analytical specifications, ensure quality control of analytical outputs, and deliver clear insights while overseeing methodologies and clinical context research.

The Principal Data Analyst will manage paid media analytics and operations, ensuring data quality, compliance, and actionable insights while collaborating with media buyers to improve performance and ROI.

Lead data and software QA for enterprise data platforms: define testing strategy, build automated test frameworks, validate ETL/ELT and data transformations, run data quality checks and reconciliations, test APIs and data products, analyze datasets with SQL to find defects, drive root-cause resolution, participate in Agile ceremonies, and mentor team members to improve quality practices.