Skip to main content

Klaviyo

Analytics Engineer

Boston, MAFrom $168kmidAdded yesterday

About this role

Senior Analytics Engineer role at Klaviyo focused on building scalable data pipelines and dimensional models that power Sales and Product analytics. You'll own end-to-end data infrastructure using dbt and Snowflake, partnering across GTM, Product, and Engineering teams to enable data-driven decision-making.

What you'll do

  • Design and maintain production-grade dbt data models and dimensional schemas for accounts, pipeline, and product usage
  • Build curated data marts and reverse ETL workflows to operationalize insights into business systems
  • Own end-to-end data pipelines including ingestion from Salesforce, ERP, and product data sources
  • Implement data quality frameworks, testing, monitoring, and documentation to ensure governance and compliance
  • Partner with Sales Ops, Product, Analytics, and Finance teams to translate ambiguous business needs into structured solutions
  • Lead design reviews, mentor team members, and maintain dbt repository best practices and scalability

What they're looking for

  • dbt and SQL with focus on dimensional modeling
  • Snowflake (or BigQuery/Redshift)
  • Python for orchestration and tooling
  • Data pipeline design and ETL/reverse ETL workflows
  • Data quality testing and governance frameworks
  • Airflow and cloud infrastructure (AWS, Terraform)
  • Cross-functional collaboration and stakeholder communication
  • Sales Operations or GTM analytics (nice to have)
Apply on the employer's site

Opens the official application on the employer’s site. No login required.

Klaviyo

Klaviyo builds a platform for personalized mobile app experiences and customer engagement, offering SDKs, identity and access management services, and onboarding solutions. The company is hiring software engineers across mobile development, identity systems, product activation, and internal platform teams.

Website
klaviyo.com
View all jobs at Klaviyo

Likely interview questions

  • Tell us about a time you owned an end-to-end data pipeline from ingestion to business impact—what challenges did you face?
  • How do you approach translating ambiguous business requirements into scalable dbt models?
Unlock all 7 questions free — and practice them live →