Senior Analytics Engineer

About the company

The company is revolutionizing the post-purchase experience for retailers and their customers by providing merchants with AI-driven solutions that enhance customer satisfaction and drive revenue growth. Our comprehensive platform offers automated customer service handling, seamless returns/exchange management, end-to-end automated fulfillment, and product protection and shipping protection alongside the company's best-in-class fraud detection. By integrating leading-edge technology with exceptional customer service, the company empowers businesses to build trust and loyalty among consumers while reducing costs and increasing profits.

Today, the company works with more than 1,000 leading merchant partners across industries, including fashion/apparel, cosmetics, furniture, jewelry, consumer electronics, auto parts, sports and fitness, and much more. The company is backed by some of the most prominent technology investors in the industry, and our headquarters is in downtown San Francisco.

About the Role

The company provides product and shipping protection to merchants and their customers, supporting the data and systems behind protection plans, claims, and payments.

You will design, build, and improve the data pipelines, integrations, and foundational data models that power the company's analytics and financial systems. Working primarily in Snowflake, dbt, and AWS, you'll ensure data from internal systems, merchants, and external partners is reliable, accessible, and structured for downstream use.

You'll partner closely with Engineering, Analytics, Finance, and Operations to solve complex data challenges, improve our infrastructure, and establish best practices. This is a hands-on senior role for someone who combines strong SQL and data engineering skills with technical leadership and independent problem-solving.

What You’ll Do

Data Architecture & Engineering. Design, build, and maintain scalable data pipelines and architecture across Snowflake, dbt, and AWS. Lead technical decisions around ingestion, transformation, orchestration, and data modeling, ensuring our infrastructure is reliable, performant, and built to scale.

Data Integrations & Foundational Modeling. Develop and improve integrations with internal systems, merchants, payment platforms, and external partners. Build well-structured, reusable dbt models that transform raw source data into trusted datasets for downstream analytics, financial systems, and reporting.

Data Quality & Governance. Establish standards for data testing, monitoring, validation, documentation, and governance. Implement automated controls to identify issues early, improve data integrity, and ensure data is traceable and reliable across systems.

Cross-Functional Partnership. Partner with Engineering, Analytics, Finance, Operations, and Financial Systems to understand business requirements, define data specifications, and ensure downstream teams have the reliable data they need. Communicate technical concepts and tradeoffs effectively to both technical and non-technical stakeholders.

Technical Leadership & Continuous Improvement. Drive engineering best practices across SQL, dbt, code reviews, CI/CD, and development workflows. Identify opportunities to optimize performance, simplify architecture, and reduce manual processes. Mentor teammates, provide technical guidance, and help shape the long-term direction of our analytics engineering platform.

AI-Enabled Development. Incorporate AI coding agents and tools into everyday development workflows. Identify repeatable processes that can be turned into reusable skills, automated checks, and agent-driven workflows, while maintaining strong engineering standards and rigorously reviewing AI-generated code.

Qualifications

Required

• 5+ years of experience as an Analytics Engineer, Data Engineer, or in a similar role focused on data infrastructure, analytics, or operational dataExpert-level SQL skills, including complex joins, window functions, advanced transformations, and query optimization across large datasets.

• Strong production experience with dbt and cloud data warehouses, preferably Snowflake, including data modeling, testing, documentation, and deployment practices.

• Deep understanding of data modeling principles, including dimensional modeling, facts and dimensions, slowly changing dimensions, and incremental models.

• Experience designing and maintaining end-to-end data pipelines, from source ingestion through transformation and downstream reporting.Experience with Git-based development workflows, including pull requests, code reviews, CI/CD, and collaborative development practices.

• Hands-on experience building with AI coding agents (e.g., Claude Code).

• Ability to independently lead technical projects, make sound architectural decisions, and balance immediate business needs with long-term platform improvements.

• Strong communication skills and the ability to work effectively across technical and non-technical teams.

Nice to Have

• Experience with AWS data services such as S3, Glue, and Lambda.

• Familiarity with infrastructure as code and cloud-based data architecture.

• Working knowledge of Python for automation and validation (the role is SQL/dbt-first).

• Experience working with APIs and external data integrations.

Expected Pay Range: $155,000 - $170,000 per year salaried*

*The target base salary range for this position is listed above. Individual salaries are determined based on a number of factors including, but not limited to, job-related knowledge, skills and experience.

Extend does not provide immigration-related sponsorship for this role.

Life at the company

• Working with a great team from diverse backgrounds in a collaborative and supportive environment.

• Competitive salary based on experience, with full medical and dental & vision benefits.

• Stock in an early-stage startup growing quickly.

• Generous, flexible paid time off policy.

• 401(k) with Financial Guidance from Morgan Stanley.

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