Top Hybrid Data & Analytics Jobs in Boston, MA

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 complex workforce analytics and strategic workforce planning initiatives for a Technology organization. Analyzes workforce, business, financial, talent, skills, and labor-market data; develops models, scenarios, and executive insights; advises senior leaders; influences workforce decisions; and advances analytics methodologies, tools, and capabilities. The role also mentors peers and manages multiple cross-functional workstreams amid ambiguity.

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.

Design, code, test, and deploy software solutions for Tax clients. Collaborate with teams to gather requirements, implement APIs and integrations, apply CI/CD and OOD principles, perform code reviews, debug and QA, and support project delivery while building client relationships and technical skills.

Own wholesale performance reporting and analysis across retail partners, products, and regions. Build datasets, data models, dashboards, and automated reporting while validating data quality. Analyze sell-through, inventory, pricing, promotions, and commercial trends to identify growth opportunities. Partner with stakeholders across Wholesale, Digital Commerce, and Marketing to frame analytical questions, communicate findings, and provide actionable recommendations.

The AI Analyst Co-op will use Python, HTML, SQL, APIs, and AI tools to generate scalable insights for go-to-market teams. Responsibilities include personalizing BDR outreach by buyer persona, distributing market insights across sales, marketing, and partner resources, and collaborating with Platform Advisory. The role requires strong analytical skills, attention to detail, problem-solving persistence, and an understanding of technology’s role in improving operational efficiency.

Design, build, test, and support scalable cloud data solutions and pipelines. Develop data platforms using Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases, and cloud technologies. Establish reusable data engineering patterns, security controls, and operational standards. Collaborate with product managers, engineers, analysts, and data scientists; communicate technical outcomes; mentor data professionals; and deliver resilient, high-performance financial data products.

Build AI agents and data acquisition systems, evaluate dataset quality, define quality KPIs, and improve data processes. Partner with product teams to deploy data into customer-facing products and acquire datasets from third parties, including coordinating vendors and contracts. The role requires organizing multiple projects, structuring analyses, communicating tradeoffs, and using data to support investment decisions and customer outcomes.

Lead analytical investigations and build measurement frameworks, dashboards, reporting systems, and reusable Snowflake-backed data products for Customer Experience teams. Partner with data scientists on health scoring, forecasting, segmentation, and validation. Translate ambiguous business questions into decision-ready insights, communicate findings to senior stakeholders, and document solutions for operational handoff.