Manager, Analytics Engineering

Extend

1d•$165,000 - $195,000•Remote

About The Position

Extend 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 Extend's best-in-class fraud detection. By integrating leading-edge technology with exceptional customer service, Extend empowers businesses to build trust and loyalty among consumers while reducing costs and increasing profits. Today, Extend 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. Extend is backed by some of the most prominent technology investors in the industry, and our headquarters is in downtown San Francisco. About the Role: Extend powers product protection, shipping protection, and warranty programs for hundreds of merchants. The data those programs generate drives pricing and actuarial modeling, risk and loss analysis, fraud detection, product and merchant analytics, and financial and revenue reporting. The Analytics Engineering team owns the platform behind all of it: the Snowflake warehouse and dbt repository the company reports from, the ingestion that feeds them, and the pipelines and monitoring that keep them running. Analytics, Actuarial, Risk, Fraud, Product, Operations, Finance, Accounting, and Revenue all build on what this team produces. We’re looking for a Manager, Analytics Engineering to lead that team. You’ll report to our Senior Engineering Manager, Data Engineering. The role is hands-on, leading a remote team of analytics and data engineers.

Requirements

2+ years managing engineers. Hiring, performance, and career development on a data or analytics engineering team. Prior management experience is expected, though we will consider lead engineers who have owned technical direction and developed the engineers around them.
Advanced SQL and dimensional modeling. You have modeled a business domain for consumers with competing needs and kept it consistent as requirements changed.
Deep dbt experience. You have owned a repository under version control with testing, PR review, CI, and change control, and you set that standard rather than working within one.
Pipeline engineering. Python and cloud data infrastructure. We run on AWS with Glue, Step Functions, Lambda, and CDK.
Reliability ownership. You have run on-call for a data platform, built alerting that teams trust, and led incident response.
Analytical partnership. You’ve supported analysts, data scientists, or quantitative teams, and can translate a business question into a data model.
Clear written communication. Architecture proposals, incident reviews, and candid tradeoff summaries for partners with different priorities.
Prioritization judgment. You prioritize deliberately across many requests and communicate the tradeoffs clearly.

Nice To Haves

actuarial, risk, or fraud analytics; warranty, insurance, or service-contract programs; financial and revenue reporting; privacy and deletion compliance at scale; BI administration; or AI-assisted engineering workflows.

Responsibilities

Manage and grow the team. Hiring, onboarding, career development, and performance.
Set the technical bar and own the dbt repository as a shared platform. Review pull requests, make the architecture calls, and set the standard for tests, documentation, and CI for your team and for every team that ships models into the repo. Partner teams own the business logic in their models; you own the platform and standards they ship into. Keep change control rigorous and fast.
Own the warehouse and pipelines. Snowflake, dbt, source ingestion and freshness, external tables, and the AWS Glue/CDK jobs that feed them.
Model the core business domains. Orders, contracts, claims, and service orders, defined once so actuarial, risk, fraud, product, and finance all get the same answer. Evolve these models as upstream product systems change, and consolidate warehouse modeling onto the shared platform.
Evolve the platform. Lead platform migrations, including moving dbt execution to Snowflake-native tooling, and retire legacy components on a planned timeline.
Partner across the business. Build trusted relationships with internal stakeholders by translating their questions into models, aligning them on shared definitions, and shaping roadmap priorities so the platform serves the whole business.
Harden data quality. Schema validation that quarantines bad records without interrupting scheduled refreshes, plus freshness and critical-service audits with named owners.
Run platform operations. On-call, monitoring, alerting, incident triage, and root-cause follow-through.
Enable self-service. Documentation, semantic consistency, and BI access so partners can answer their own questions.
Automate operational work. Extend the AI-assisted workflows already running in production for alert triage, refresh requests, and file processing.