Top Data & Analytics Jobs in Boston, MA

Leads the design and modernization of scalable data architecture for economic data platforms, including massive time-series datasets, archival documents, and research publications. Responsibilities include data modeling, metadata and governance design, storage and processing architecture, indexing, discoverability, security, lifecycle management, and API data access optimization. Partners with application architecture and cross-functional teams to deliver highly available, secure, public-facing data platforms supporting analytics and research.

Leads applied AI and health data architecture initiatives for payer clients. Designs cloud-native platforms, pipelines, data models, integrations, and modernization roadmaps across AWS, Azure, Databricks, and Snowflake. Oversees data quality, governance, anonymization, performance optimization, and legacy migrations while advising stakeholders on AI-ready architectures. Manages multi-team delivery, resolves technical issues, presents solution tradeoffs, and coaches practitioners.

Leads the strategic direction of data initiatives, governance, and decision-making frameworks. Establishes growth and innovation strategies, manages transformative data projects, maintains executive client relationships, mentors leaders, promotes innovation, collaborates across departments, and ensures adherence to professional data-management standards. The role also supports business development and applies modern cloud architectures, integration patterns, big data engineering, advanced analytics, and AI/ML capabilities.

Leads strategy, architecture, governance, sourcing, and delivery of training and calibration data for data science and pricing teams. Oversees data engineering and pipeline modernization, vendor and warehouse integration, source-system changes, data lineage, and model-ready dataset delivery. Manages the Modeling Data Solutions organization, partners with enterprise data stakeholders, and guides teams supporting business-line data needs.

Designs and maintains reusable data ingestion and transformation components, automated quality and governance checks, reliable pipelines, monitoring, deployment automation, cloud integrations, containers, and infrastructure as code. Partners with data consumers on usable models and datasets, documents standards and runbooks, supports Agile improvement, and responds to critical IT incidents. Requires 5+ years of data engineering experience, orchestration, distributed processing, storage, scripting, automation, and version control expertise.

Analyze how customers use Hi Marley to guide product, customer success, and company decisions. Define feature success metrics, evaluate adoption funnels, measure pilots and utilization, present customer-facing insights, and establish trusted self-service reporting. Partner with Data Engineering to translate analyses into governed data models and communicate findings clearly to executives and customers.

Leads the Sales Analytics team supporting Sales and Customer Success across three markets. Oversees business intelligence tools, dashboards, sales data modeling, retention and revenue analysis, and go-to-market measurement. Modernizes data workflows using Snowflake, dbt, Git, and tested models; develops Gong transcript use cases with LLM-based extraction; stewards Salesforce data; and partners with Sales, Revenue Operations, Engineering, and leadership to drive data-informed decisions.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Responsibilities include data modeling, architecture, distributed processing, relational and NoSQL databases, data security, observability, and real-time workloads. The role collaborates with product and engineering teams, establishes reusable engineering patterns, communicates technical concepts, and mentors data professionals while delivering resilient solutions.

Build and operate production data pipelines, models, and data products supporting finance, accounting, treasury, compliance, and regulatory reporting. Partner cross-functionally to define requirements, model on-chain and internal data, and ensure data reliability through reconciliation, quality checks, monitoring, and alerting. Use SQL, Python, cloud data warehouse, Airflow, dbt, and AI tooling. Mentor engineers, review technical work, document systems, and communicate data concepts to technical and non-technical audiences.

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.

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.