Top Senior Data & Analytics Jobs in Boston, MA

Designs and develops scalable, reusable data solutions, production pipelines, reporting dashboards, and proof-of-concepts for business stakeholders. The role includes data engineering, infrastructure optimization, data quality testing, metadata and lineage management, compliance, security, and Gen AI applications. Responsibilities also include evaluating tools, mentoring junior team members, prioritizing customer-focused solutions, and collaborating with technical and non-technical stakeholders.

Designs and develops scalable, reusable data solutions, production pipelines, dashboards, and reporting tools. Builds proof-of-concepts, optimizes data infrastructure, implements data quality testing, and applies metadata, lineage, compliance, and security principles. The role also evaluates data engineering technologies, explores generative AI applications, supports stakeholder needs, prioritizes customer value, and mentors junior team members.

Designs and develops scalable, reusable data solutions, production pipelines, reporting dashboards, and proof-of-concept systems. Responsibilities include data engineering, infrastructure optimization, data quality testing, metadata and lineage management, compliance, security, GenAI applications, and iterative solution testing. The role collaborates with technical and non-technical stakeholders, prioritizes customer value, mentors junior team members, and evaluates emerging data engineering tools and technologies.

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.

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

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.

Lead commercial analytics to diagnose sell-through, pricing, promotions, assortment, and retailer execution. Build causal and incrementality measurement, design test-and-learn, support elasticity and margin analyses, and deliver executive-ready insights and frameworks to optimize revenue, margin, and promotional efficiency across channels and retailers.

Support Business Banking with analytics, reporting, dashboards, and automation to drive deposit acquisition, onboarding, funded balance growth, and retention. Produce forecasting and pipeline reporting, provide deposit strategy and profitability insights, enable go-to-market and campaign analysis, and standardize onboarding and data processes to improve efficiency and data quality.

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.

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.

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

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.