Top Analytics Engineer Jobs in Boston, MA

Build and maintain scalable dbt models in Snowflake, implement data quality tests and documentation, collaborate with Analytics and Data Engineering on ingestion and warehouse optimization, support data governance and apply software engineering best practices to analytics code.

Own end-to-end data pipelines and production-grade dbt models (Snowflake) for Sales and Product analytics. Build governed data marts, dimensional models, and reverse ETL workflows. Improve data quality, testing, monitoring, and documentation. Partner with GTM, Product, and BI stakeholders, mentor peers, and contribute to platform tooling (Airflow, Terraform, AWS) to enable reliable, scalable analytics.

Design, build, and maintain end-to-end data models and pipelines to support CX operations, compliance reporting, and customer insights. Create dashboards and metrics, enforce data quality and governance, automate recurring workflows, and partner with stakeholders and broader data teams to deliver self-serve analytics solutions.

Build, refactor, and operate a production RWD delivery pipeline using dbt, PySpark, Snowflake, and AWS. Own delivery endpoint configs as code, enforce HIPAA de-identification, manage monthly deliveries, drive data quality metrics and investigations, support channel partners, and collaborate with platform engineering using CI/CD, Argo orchestration, and observability tools.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.

Lead technical design and architecture for analytics engineering: build and govern the semantic/metrics layer, optimize dbt and Snowflake for performance and AI use cases, mentor engineers, partner with Data Science and Engineering, drive data quality, CI/CD, observability, and deployment of third-party data and emerging tooling to support analytics, ML, and agent-based products.

Design and build Toast’s enterprise analytics layer and AI-ready semantic layer in the data warehouse. Collaborate with domain teams and data engineering to implement hub-and-spoke architecture, define complex business logic in SQL, support scalability, partner with stakeholders, and provide technical mentorship.

Build and own the Snowflake/dbt data infrastructure for WHOOP's financial planning model: standardize financial actuals, develop Snowflake-native functions and stored procedures, translate FP&A modeling into version-controlled SQL/dbt models, integrate source systems, implement data quality tests, support Sigma front-end, document lineage, and apply software engineering best practices while implementing AI-augmented forecasting and automation.

The Analytics Engineer will build and optimize data transformation models, assist with BI tools, support stakeholders, document processes, and implement AI tools, contributing to a data platform under mentorship.

The Data Engineer will design and build data solutions, develop ELT jobs, ensure data quality, and collaborate with technical teams to align data strategies with business goals.

The Senior Analytics Engineer will develop data infrastructure, optimize data systems, ensure data quality, and collaborate on scalable data models and pipelines, focusing on analytics support.

Design and own end-to-end data models and dbt transformations on BigQuery, maintain the semantic layer in Lightdash, govern metrics, enable AI-assisted analytics workflows, partner with stakeholders to deliver trusted reporting, and lead data modeling standards, testing, and documentation.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.

The role focuses on implementing cost optimization solutions for cloud infrastructure through automation and collaboration across teams, enhancing FinOps practices.

Build AI-augmented BI dashboards, semantic layers, and AI-ready metadata. Use AI-assisted tools to accelerate SQL and dashboard development, productionize ML outputs into BI, enforce data quality and governance, and present insights to business stakeholders including C-suite.

Design, build, and maintain scalable ELT pipelines and analytical data models. Ensure data quality, validation, and documentation. Collaborate with analysts, data scientists, and engineers to deliver reliable datasets and tooling for reporting and decision-making. Monitor, troubleshoot, and enforce data governance and security; finance domain experience is a plus.

Design and implement ad measurement metrics, perform deep-dive analyses, build reporting and automated pipelines, create visualizations, and provide actionable insights to improve advertiser measurement and platform health.

Partner with Content Finance stakeholders to build and own end-to-end data products and semantic models. Define core metrics, maintain documentation and tests, perform root-cause analysis, improve pipelines, and drive cross-functional alignment. Mentor teammates, champion data-as-product practices, and leverage GenAI tools to accelerate development.

The Senior Analytics Engineer will develop data pipelines, build models, and create dashboards to provide actionable insights for various teams. The role requires collaboration with multiple stakeholders to meet analytical needs and ensure data governance.

Build and own the reporting and insight layer: write optimized Redshift queries, build dashboards, monitor model feedback signals, triage and prioritize investigations for data science, maintain ETL pipelines ingesting semi-structured data, and enable Customer Success with clear metrics and onboarding checks. Move into model validation and automation to scale analytic workflows.

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.

Build and maintain auditable data pipelines in Snowflake and dbt to ensure financial reconciliation and audit readiness. Partner with Accounting, Operations, Engineering, and Reporting to investigate data issues, improve integrations via APIs, and own claim reimbursement and contract sales data workflows.

As a Billing & Analytics Software Engineer, you'll design and maintain the billing pipeline, integrate billing systems, and develop customer-facing analytics dashboards to support revenue recognition and operational observability.

Bridge enterprise customers' legacy BI to Hightouch by reverse-engineering complex marketing reports, building reusable reporting frameworks, engineering and documenting semantic/context layers, ensuring accurate SQL generation, and training non-technical marketers while collaborating cross-functionally to deliver reliable, idempotent AI-driven analytics.

Design, build, and optimize modular dbt pipelines and data models; translate ambiguous product requirements into production-grade data assets; diagnose query performance and lower warehouse compute costs (including migration to Snowflake); enforce code hygiene, data security, testing, and peer review; mentor junior engineers and adopt AI-assisted development tools to speed test generation and legacy discovery.