Top Data & Analytics Jobs in Boston, MA

Leads data engineering across investment technology squads while remaining hands-on. Designs scalable Snowflake and AWS data platforms, market-data pipelines, data models, testing strategies, and quality standards. Applies AI and agentic tools to improve engineering workflows, partners with product and technology leaders on data roadmaps, and mentors engineers. The role supports institutional investment and public-markets research data products.

Build and operate scalable data pipelines and models on Snowflake and AWS for Global Liquid Markets investment products. Responsibilities include integrating market and vendor data, implementing automated testing and governance standards, developing AI-powered engineering workflows, collaborating with product and business stakeholders, contributing to the data roadmap, mentoring engineers, and participating in code reviews. The role requires strong communication, architecture, agile delivery, data engineering, and responsible AI skills.

Analyzes financial risk data and develops data transformation, reporting, dashboard, and visualization solutions for banking and capital markets clients. Supports quantitative analytics involving capital, liquidity, credit risk, and asset-liability management. The role also includes client collaboration, quality review, mentoring junior staff, process improvement, and automation of data movement and reporting.

Leads quantitative and qualitative consumer research projects, brand-health tracking, audience measurement, and recurring insights programs. Analyzes surveys, behavioral metrics, syndicated data, and competitive intelligence to identify trends and business implications. Creates reports and presentations that translate findings into recommendations for marketing, brand, product, and customer experience teams. Manages survey platforms, research quality, vendors, timelines, and cross-functional stakeholder communication.

Build and architect scalable cloud data platforms, pipelines, and applications using technologies such as Python, Spark, Databricks, and Snowflake. Lead large-scale data initiatives, make architectural decisions, establish engineering patterns, and ensure platform resilience and operational efficiency. Collaborate with product, engineering, analytics, and data science teams while mentoring engineers and communicating technical concepts to stakeholders. Evaluate data platform choices and deliver solutions supporting machine learning, distributed systems, and evolving enterprise data needs.

Leads advanced analytics, reporting, scalable data models, dashboards, and data pipelines supporting Business and Marketing teams. Partners with senior stakeholders and AI Engineering to implement AI and machine learning workflows, predictive insights, and automation. Develops documentation, drives analytics engineering best practices, and mentors team members. Requires strong SQL, Python, APIs, data modeling, stakeholder communication, project leadership, and blockchain expertise.

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 and scales Hi Marley’s analytics function, managing analysts and hiring as the team grows. Establishes trusted company metrics, self-service analytics, standards, and an analytics roadmap. Partners with Product, Customer Success, Growth, Marketing, Finance, and Data Engineering on product, customer, revenue, financial, billing, marketing, experimentation, and utilization analysis. Develops people, communicates insights to executives, and drives data-informed decisions across the organization.

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.

Leads the Journey Intelligence Team’s analytics strategy, develops analysts, and builds measurement frameworks, segmentation strategies, and scalable data assets. Conducts exploratory analysis, experimentation, and customer lifecycle research to identify engagement opportunities. Partners with Product, Operations, and senior leadership to translate customer behavior insights into strategic initiatives and measurable improvements across the customer journey.

Develop market intelligence, forecasting, statistical models, scenario analyses, and automated reporting for supply chain decisions. Integrate internal and external datasets using Python, R, SQL, and other tools; build dashboards; support data governance; and translate analytical findings into recommendations for procurement, planning, and executive stakeholders.