Analytics Engineer

At Klaviyo, we value the unique backgrounds, experiences and perspectives each Klaviyo (we call ourselves Klaviyos) brings to our workplace each and every day. We believe everyone deserves a fair shot at success and appreciate the experiences each person brings beyond the traditional job requirements. If you’re a close but not exact match with the description, we hope you’ll still consider applying. Want to learn more about life at Klaviyo? Visit klaviyo.com/careers to see how we empower creators to own their own destiny.About the Team Data is at the heart of every decision at Klaviyo. Our Analytics Engineering team sits within a hub-and-spoke model, partnering closely with Go-To-Market (GTM), Product, Engineering, and Business Intelligence teams to build scalable, trusted data systems. You’ll work at the intersection of business and engineering, owning core data models, enabling self-service analytics, and ensuring our data ecosystem is reliable, scalable, and compliant. About the Role We’re looking for a Senior Analytics Engineer to own end-to-end data pipelines and modeling across GTM (Sales) and Product domains. You’ll build and maintain the source of truth for operational and product data, enabling leadership, analysts, and product teams to answer complex questions with confidence. This role combines deep technical execution with high-impact business partnership, requiring you to translate ambiguous needs into scalable data products. You will operate as a trusted, embedded partner to stakeholders while leveraging the broader Data Engineering organization’s standards, tooling, and best practices. You’ll help build the foundation that powers everything from forecasting and compensation to product insights and experimentation. How You’ll Make a Difference Own and deliver end-to-end data pipelines and models that power Sales and Product analytics Build curated, governed data marts that enable fast, reliable decision-making Design dimensional data models (dbt) for core entities such as accounts, pipeline, performance, and product usage Partner with Sales, Product, and Analytics teams to create holistic views of the customer and product lifecycle Raise the bar on data quality, testing, monitoring, and documentation Enable reverse ETL workflows to operationalize insights into business systems Act as a data ambassador across Product, Engineering, BI, and Legal, ensuring alignment on data contracts and governance Lead and mentor others through design reviews, best practices, and technical guidance Evolve internal tooling and infrastructure to improve developer and analyst productivity What You’ll Do Data Modeling & Pipelines: Build and maintain production-grade dbt models in Snowflake Integrations & Ingestion: Own ingestion from systems like Salesforce, ERP, and product data sources Reverse ETL: Push high-value data models into downstream tools and workflows Data Quality & Governance: Implement testing frameworks, monitoring, and documentation to ensure trust and compliance Cross-Functional Partnership: Work closely with Sales Ops, Product Managers, Engineering, Finance, and GTM teams Repository Stewardship: Maintain dbt codebase, enforce best practices, and ensure scalability Tooling & Infrastructure: Contribute to Airflow, Terraform, AWS, and other platform tools as needed Who You Are 5+ years of experience in analytics or data engineering in a modern data stack (Snowflake, BigQuery, or Redshift) Strong expertise in SQL and dbt, with a focus on building scalable, production-grade data models Proficiency in Python (or similar) for orchestration or tooling Experience owning end-to-end data workflows, from ingestion to business impact Ability to translate ambiguous business needs into structured data solutions Strong communication skills with the ability to partner with non-technical stakeholders Experience implementing data testing, monitoring, and governance frameworks Proven ability to lead cross-functional projects with minimal guidance Nice to Have Experience with Sales Operations, compensation, or GTM analytics Familiarity with reverse ETL tools and workflows Experience with Airflow, Fivetran, Workato, or similar tools AWS experience (S3, EC2, Lambda) and infrastructure-as-code (Terraform) Exposure to experimentation frameworks or product analytics systems Experience with