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.

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.

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.

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 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.

Build and maintain production-grade data models and pipelines for the Compliance Data Mart, implement data quality frameworks and monitoring, partner with engineering to resolve data gaps, support regulatory exams and audits, and automate manual workflows into scalable pipelines and self-serve tooling.

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.

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.

Design, build, and maintain scalable dimensional data models, semantic layer, and production transformation pipelines. Collaborate with Data Engineering, Data Science, BI, DevOps, and Product to enable AI-ready data, ensure data quality and governance, support ML feature delivery, and coach analytics team members for robust, production-ready data products.

Lead design and implementation of shared, reusable analytics data models and pipelines. Drive standardization, governance, observability, and CI/CD for analytics; partner with Data Science, Infrastructure, and Product to certify metrics, modernize orchestration, and integrate AI-native tooling.

Lead design and implementation of Affirm's Lakehouse Platform using Iceberg, Spark, Snowflake, and cloud storage. Drive platform roadmap, governance, access controls, performance and cost optimization, analytics engineering best practices, observability, and on-call readiness. Partner cross-functionally, mentor engineers, and build scalable, secure, self-service data infrastructure and semantic/agentic data tools.

The RevOps Analytics Engineer is responsible for maintaining data infrastructure, building reliable data systems, and supporting revenue analytics through effective data modeling and ETL/ELT processes.

Senior AWS Analytics Engineer. Posting contains no detailed responsibilities or qualifications beyond the job title.

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

Build, operate, and extend data platform and application-layer services enabling secure, self-service data applications at scale. Implement provisioning, CI/CD, access control, integrations, observability, automation, and incident response while contributing to semantic layer and AI-ready tooling roadmaps.

Own and architect the semantic layer and metrics/metadata for the data platform (Databricks/GCP). Build AI-ready models, enforce modeling and documentation standards, implement observability/testing for pipelines, enable self-serve analytics, and collaborate across product, clinical, and business teams to translate questions into reliable data solutions.

Perform cross-product credit analytics to inform underwriting, limits, and portfolio strategy. Build customer credit profiles, run experiments (A/B tests), uncover insights, recommend and execute policy changes, and communicate results to leadership. Leverage internal and external data, predictive techniques, and AI tools to optimize approvals, risk, and product performance while balancing growth and loss.

Build and maintain GCP-based data pipelines and foundational tables (BigQuery, Dataform, Dataplex). Merge financial and ERP datasets, define metrics, deliver reports and analyses, and collaborate cross-functionally to support decision-making and business measurement.

Develop and improve inline metrology flows and failure-analysis methods for photonic quantum hardware. Build and analyze SPC charts, perform RCA, design experiments, coordinate with software and external partners, purchase and qualify metrology tools, and apply statistical analysis to improve yield and wafer performance.

Design, build, and deliver a global analytics platform: manage data collection and pipelines, develop visualizations and reports, create semantic models, tune performance, collaborate with IT and business stakeholders, and implement governance for analytics products.

Partner with stakeholders to define metrics and deliver dashboards and insights. Build and optimize data models in Amazon Redshift, design and maintain scalable ETL pipelines using Python and Apache Airflow, monitor data quality and pipeline performance, and participate in code reviews and engineering best practices to support analytics and reporting needs.

Owner of the analytics foundation: design and maintain BigQuery/dbt models, pipelines, and Looker Studio reporting; build AI workflows and web apps on GCP; integrate marketing, e-commerce, and fulfillment data; ensure GCP ops, security, and reliability; use AI-assisted tools daily and collaborate with business and client stakeholders.

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.

Design and maintain dbt data models, optimize pipelines with data engineers, translate business requirements into technical specs, enforce code quality and testing, and document a company-wide data dictionary to support reliable reporting and dashboards.