Top Hybrid Data & Analytics Jobs in Boston, MA

Supports client portal data, reporting, dashboards, and visualizations for investment data. Translates business needs into requirements, mappings, user stories, and acceptance criteria; validates Power BI and Snowflake data; performs reconciliation and root-cause analysis; supports production incidents, releases, and documentation; and coordinates an offshore support partner across priorities, testing, issue resolution, and operational readiness.

Leads commercial data governance strategy, operating models, policies, standards, ownership, stewardship, metadata, lineage, and data-quality programs. Partners with cross-functional stakeholders to improve data integrity, accessibility, privacy, compliance, and responsible use. Oversees governance councils, catalogs, glossaries, issue remediation, performance metrics, tool adoption, training, and change management across a regulated biotechnology commercial organization.

Develops marketing dashboards, reporting, attribution models, and funnel analysis to guide campaign investment and pipeline decisions. Ensures data quality across Salesforce, marketing automation, BI, and warehouse systems; resolves discrepancies and supports scalable data foundations. Builds API-based automations and applies AI/LLM capabilities to improve analytics workflows. Partners with marketing, revenue operations, sales, and leadership to translate marketing performance data into actionable business recommendations.

Manages research data acquisition, licensing, contracts, vendor relationships, governance, and compliance at Harvard Kennedy School. Leads data safety staff, evaluates contractual and privacy risks, develops data management plans and SOPs, oversees access and disposition workflows, and advises researchers and institutional stakeholders on data-use obligations, regulatory requirements, and risk mitigation.

Own enterprise client reporting and analytics for influencer marketing campaigns. Analyze creator, content, audience, platform, and campaign data to identify performance drivers and translate findings into actionable recommendations. Build client-facing reports, executive presentations, scalable dbt and BigQuery data models, standardized metrics, reusable data assets, and quality checks. Partner with Client Success, Services, Sales, data engineering, and product teams to support client value, renewals, expansion, and scalable analytics frameworks.

Leads and develops a data engineering chapter while remaining hands-on in an agile squad. Responsibilities include coaching specialists, defining future skills, designing scalable data pipelines and infrastructure, establishing engineering standards, partnering on architecture, improving data quality, supporting governance and privacy, and collaborating with data scientists, analysts, and business stakeholders. The role also supports performance management, workforce planning, recruitment, and talent development.

Designs, develops, and maintains enterprise ETL solutions using Informatica, SQL, cloud platforms, and data warehousing practices. Builds scalable data pipelines, integrates Salesforce and other enterprise systems, monitors data quality and performance, troubleshoots issues, and supports cloud modernization. Collaborates with business and technology teams in a regulated financial services environment while contributing to technical standards, documentation, governance, and mentoring junior developers.

Build and maintain scalable data pipelines, models, and cloud infrastructure supporting business performance analysis. Collaborate with data scientists, ML engineers, product teams, and business stakeholders to translate requirements into technical solutions. Develop self-service data tooling, optimize Snowflake and AWS environments for scale and cost, and support deployment through CI/CD and DevOps practices. The role also involves data integration, modeling, documentation, and ensuring data integrity across global entities.

Principal technical leader responsible for architecting and operating relational data marts, dbt data products, semantic layers, and shared data services. Builds scalable solutions in Databricks, Delta Lake, and Spark; owns data products through testing, deployment, documentation, monitoring, and support. Establishes governed metrics, data quality, lineage, observability, and access controls while partnering cross-functionally to resolve ambiguity. Mentors engineers, improves reusable frameworks, and supports BI, data science, and AI use cases.

Build and own analytical data products, dbt models, semantic layers, shared metrics, and data quality systems. Model GA4 and digital analytics data, activate trusted data through reverse ETL, and partner with analysts, product teams, and business stakeholders to support decisions. Mentor engineers and analysts, establish modeling standards, and contribute to self-service and AI-assisted analytics foundations.