Senior Software Engineer - Analytics Data Engineering

Senior Software Engineer, AI & Analytics Data Enablement

Location: Boston, MA

As a Senior Software Engineer on Klaviyo’s AI & Analytics Data Enablement team, you’ll build and own the data foundations that power our Analytics & AI engine. You’ll design resilient pipelines and analytics models that turn billions of events and domain objects into trusted, actionable insights for Analytics and AI product features. "Your work will help our customers understand the performance of their marketing and power AI & Analytics experiences for our customers."

About the Role

You’ll join a cross-functional R&D team focused on the availability, quality, and usability of AI and Analytics data for our customers. In this Senior-level role, you’ll lead complex software and data engineering projects across multi-component systems, from shaping requirements to design, implementation, and long-term operation. You’ll be a go-to expert for AI and analytics data, collaborating with product managers, AI, analytics, and other engineering teams to ensure our data is accurate, observable, and ready to drive insights and enable AI/ML products at scale.

You’ll also help evolve our technical standards for data engineering, raising the bar on data modeling, performance, and reliability, while mentoring other engineers on best practices and modern data tooling.

What You’ll Do

Lead the design and implementation of robust, scalable data pipelines and core tables for Klaviyo. These pipelines are crucial for powering customer facing analytics features and powering AI models to unlock the next stage of Klaviyo's products

Partner with Product, Engineering, and AI/ML teams to define clear, consistent metrics and data contracts that align to business goals and product outcomes.

Transform workflows by putting AI at the center, building smarter systems and ways of working from the ground up for example, using AI to generate tests, detect anomalies, summarize data issues, or accelerate analysis.

Mentor other engineers in data engineering best practices, code quality, observability, and system design; contribute to shared libraries, patterns, and tooling that improve developer velocity across R&D.

Who You Are

6+ years of software engineering with at least 4 or those being data engineering experience, with a strong track record building and operating data pipelines and analytics models in production.

Deep proficiency in large-scale SQL and data modeling for analytics. 

Strong programming skills in a modern language commonly used for data engineering (e.g. Python), and familiarity with orchestration and transformation tooling (e.g., Airflow, EMR, Spark/pyspark, or equivalents).

Experience working with modern data platforms (e.g., columnar data warehouses, object storage) and processing frameworks (batch and/or streaming).

Demonstrated ability to lead complex projects spanning multiple components and engineers—driving clarity, making tradeoffs, and delivering measurable impact on reliability, performance, or business outcomes.

Comfortable owning systems in production: you think about observability, incident response, incremental rollouts, and long-term maintainability as part of the design.

Excellent communication skills; you can explain data tradeoffs and constraints to non-technical partners and influence decisions with clear reasoning.

You’ve already experimented with AI in work or personal projects, and you’re excited to dive in and learn fast. You’re hungry to responsibly explore new AI tools and workflows, finding ways to make your work smarter and more efficient.

Inclusive, low-ego collaborator who values diverse perspectives and is motivated by helping the team succeed, not just shipping your own code.

Nice to Haves

Experience in a product-led SaaS environment with large-scale event data

Experience in a customer facing data engineering role supports AI/ML features and Analytics Dashboards.

Hands-on work with analytics engineering tools and practices (e.g., dbt, metrics layers, semantic models).

Experience building near real-time or streaming pipelines for user-facing analytics or monitoring.

Domain experience in martech, marketing automation, or customer engagement platforms.

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