Top Analytics Engineer Jobs in Boston, MA

Design and build scalable data foundations, pipelines, semantic models, and AI-grounded tooling. Lead platform-level AE tooling, reusable dbt patterns, observability, cost strategies, and agentic AI workflows. Partner with stakeholders, guide team technical development, and drive cross-functional data initiatives from ambiguous problems to production.

Design and implement end-to-end Microsoft Fabric data solutions (ingestion, Lakehouse, warehouse, real-time). Create architectures, reusable frameworks, and governance patterns for insurance datasets. Lead ingestion pipelines, optimize performance/cost, integrate with Azure services and security, support regulatory compliance, and provide technical direction to engineering and analytics teams.

Design and implement end-to-end data solutions on Microsoft Fabric and Azure for insurance datasets. Build ingestion pipelines (batch, streaming, API), Lakehouse architectures, and data models. Enforce governance, security, compliance, and cost/performance optimizations while guiding engineers and stakeholders on architectural best practices.

Build and maintain data infrastructure, integrate veterinary partner systems, develop dbt models and pipelines, provide analytics modeling, testing, reporting, and deliver actionable insights to customers and internal teams.

The Senior BI Analytics Engineer will design and deliver data models for analytics, ensuring data quality, and collaborating across teams to enhance AI-driven reporting and querying capabilities.

Design and maintain scalable dbt data models and Looker dashboards; partner with Finance, Product, and GTM to translate requirements into analytics solutions; ensure data quality with Data Engineering; perform analyses, forecasting, and QA to deliver trusted reporting and drive analytics initiatives.

Lead full-stack analytics at Air by designing and maintaining dbt models and BigQuery transformation layer, ensuring data quality and observability, owning ingestion/orchestration, building self-serve BI (Mode/Hex) and AI-native interfaces, and partnering with product, growth, and GTM teams to deliver trusted data products and strategic insights.

The Enterprise Performance Analytics Engineer builds and maintains data pipelines and models, ensuring data quality and efficient reporting for analytics teams.

As an Analytics Engineer at Axios, you'll design and maintain data products and visualizations, improving decision-making through actionable insights.

Hands-on engineering role to design, build, and maintain scalable ETL/ELT data pipelines, data warehouses and lakes, and data models. Optimize batch and near-real-time processing, implement query optimization and data quality checks, support AI data workflows, and collaborate with architects, analysts, and stakeholders.

Design, build, and maintain scalable, documented data models and the Gold data layer using dbt/Coalesce. Ensure data quality through automated tests, observability, version control, and CI/CD. Optimize SQL and warehouse performance, collaborate with Data Engineering and Analytics, and support semantic layers and BI catalogs to enable trusted, self-service insights for Product, Finance, and GTM teams.

Build and maintain production data pipelines and foundations for People Analytics and AI-driven tools. Integrate HR systems (Workday, Greenhouse), design data models, ensure governance for sensitive employee data, support data science and reporting, and deliver dashboards and Streamlit apps. Optimize queries, enable LLM consumption with quality and latency controls, and collaborate cross-functionally to transition AI prototypes to production.

Build and maintain analytics pipelines, data models, dashboards, and analyses to measure product metrics and business outcomes. Partner with Product, Engineering, and stakeholders to define metrics, evaluate initiatives, enable self-service analytics, and establish analytics best practices for scalable, performant data systems.

The Analytics Engineer will design, build, and maintain scalable data models, collaborating with various teams to ensure data quality and robust reporting, mainly using dbt and SQL.

Own and improve dbt models, ELT pipelines, and warehouse architecture (Snowflake or equivalent) to deliver reliable executive KPIs and metric governance. Build data spine for Finance/Operations, enforce data quality/testing, partner cross-functionally with Engineering, FP&A, and Accounting, and influence analytics engineering standards, tooling, and roadmap.

Design and build a modern data foundation: develop and scale ETL/ELT pipelines, dbt models and macros, semantic/metric layers, and analytical apps. Ensure pipeline reliability and observability, document systems, gather stakeholder requirements, and contribute to cross-functional analytics and ML initiatives.

The Senior Analytics Engineer will build and maintain data foundations for corporate reporting, translating business requirements into data models, ensuring consistent metric definitions and investigating data discrepancies for reliable reporting.

Own the Finance & Operations data layer: ingest and model data from systems (NetSuite, Ramp, Pigment) into Snowflake/Omni, ensure data quality and metric governance, build dashboards and reports, collaborate with Finance/Ops stakeholders, and automate reconciliation and reporting workflows.

Build internal dashboards, data pipelines, integrations, and lightweight apps to automate workflows and improve visibility across operations, finance, HR, and programs. Rapidly prototype AI-assisted tools, connect systems via APIs/databases/spreadsheets, ensure data quality, and collaborate with non-technical stakeholders to deliver practical analytics solutions.

Own and improve the dbt transformation layer, semantic layer, data observability, and pipeline orchestration while migrating the platform to AWS. Ensure data quality and governance to support clinical AI, collaborate with clinical, operations, and finance stakeholders, and audit, document, and fix existing data pipelines.

Partner with portfolio-company leaders to translate business problems into quantitative frameworks, build predictive models and analyses, and drive strategy and execution. Lead AI assessments, identify growth opportunities, present recommendations to executives, and implement measurable data-driven initiatives across finance, pricing, operations, and marketing.

The Senior Analytics Engineer will strategize data initiatives for secure payment solutions, crafting data models and KPIs while collaborating cross-functionally to enhance product insights and analytics capabilities.

Build and maintain electricity cost modeling REST APIs and DBT data pipelines, translate SME requirements into designs, participate in technical reviews, improve engineering processes, and collaborate across remote squads to deliver energy analytics and battery performance simulations.

Build and maintain electricity cost modeling REST APIs and DBT-driven data pipelines. Translate SME requirements into engineering designs, participate in design reviews, improve engineering practices, and deliver scalable analytics and battery performance simulations to inform DER ROI.

Implement, QA, and document analytics tags across GTM, GA4, server-side pipelines, Quantum Metric, Dynamic Yield and marketing pixels. Translate Figma into developer-ready data layers, collaborate with engineering to ensure tracking parity (web, app, Shopify), maintain pixel integrity, support experimentation piping, monthly audits, consent enforcement, and contribute to QA and solution documentation.