Senior Analytics Engineer

Extend

2d•$155,000 - $170,000

About The Position

Extend provides product and shipping protection to merchants and their customers, supporting the data and systems behind protection plans, claims, and payments. The Senior Analytics Engineer will design, build, and improve data pipelines, integrations, and foundational data models that power Extend's analytics and financial systems. Working primarily with Snowflake, dbt, and AWS, this role ensures data from internal systems, merchants, and external partners is reliable, accessible, and structured for downstream use. The role partners closely with Engineering, Analytics, Finance, Operations, and Financial Systems to solve complex data challenges, improve infrastructure, and establish best practices. It is a hands-on senior position combining strong SQL and data engineering skills with technical leadership and independent problem-solving. The engineer will lead technical decisions, improve data quality and governance, guide engineering practices, mentor teammates, and incorporate AI coding tools into development workflows while rigorously reviewing generated code.

Requirements

At least 5 years of experience as an Analytics Engineer, Data Engineer, or in a similar role focused on data infrastructure, analytics, or operational data.
Expert-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, such as 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 ability to work effectively across technical and non-technical teams.

Nice To Haves

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.

Responsibilities

Design, build, and maintain scalable data pipelines and architecture across Snowflake, dbt, and AWS.
Lead technical decisions regarding data ingestion, transformation, orchestration, and modeling, ensuring infrastructure is reliable, performant, and scalable.
Develop and improve integrations with internal systems, merchants, payment platforms, and external partners.
Build reusable dbt models that transform raw source data into trusted datasets for analytics, financial systems, and reporting.
Establish standards for data testing, monitoring, validation, documentation, and governance.
Implement automated controls to identify issues early and improve data integrity, traceability, and reliability across systems.
Partner with Engineering, Analytics, Finance, Operations, and Financial Systems to understand business requirements and define data specifications.
Communicate technical concepts and tradeoffs effectively to technical and non-technical stakeholders.
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 the analytics engineering platform.
Incorporate AI coding agents and tools into development workflows; identify repeatable processes for reusable skills, automated checks, and agent-driven workflows while maintaining engineering standards and reviewing AI-generated code.