Principal Data Engineer

Extend

16h•Onsite

About The Position

We're looking for a Principal Data Engineer to help own the analytics data architecture at Extend. This architecture powers reporting, financial processes, and business decisions for teams across the company, and feeds the data our merchants and downstream systems rely on. This is a cross-organizational role. You’ll partner with product engineering and architecture on the data flowing upstream into Snowflake, own the design and evolution of the warehouse and reporting layer in the middle, and bridge to analytics engineering and stakeholders on the consumption side. It’s a hands-on technical leadership role anchored in Snowflake and SQL, with ownership of a portfolio of Python data jobs running on AWS — work you’ll set direction on and drive end-to-end.

Requirements

10+ years in Data Engineering, Analytics Engineering, or related fields, operating at a Principal or equivalent level.
Deep relational database architecture and data modeling expertise.
Expert-level Snowflake and SQL, with experience owning a warehouse at scale.
Strong analytics engineering experience, ideally with dbt.
Solid hands-on Python, with experience building data jobs on AWS Glue, Lambda, and Step Functions, and managing that infrastructure in AWS CDK.
Experience integrating with third-party APIs in both directions, including rate limits, retries, authentication, and idempotency.
Track record of building observable, reliable data systems.
Demonstrated technical leadership and mentorship with strong communication, systems thinking, and a track record of engaging stakeholders across an organization to drive cross-functional outcomes.

Responsibilities

Database Architecture: Own our data warehouse and the reporting layer on top of it, setting patterns for how data is modeled, evolved, and exposed.
Analytics Engineering: Write SQL and dbt models, refactor transformations, and build the tables and views downstream teams rely on.
Cross-Functional Partnership: Proactively engage with teams across the company to understand how data is created and used, identify gaps, and guide solutions. Act as the connective tissue between product engineering, architecture, analytics, and business stakeholders.
Platform Architecture: Partner with DevX and architecture teams on the boundary between product engineering services and Snowflake, including leading efforts to automate schema propagation so changes upstream flow cleanly into the warehouse without manual intervention.
Data Quality: Build models, tests, and processes that anticipate malformed data and upstream changes, making pipelines reliable.
Observability & Reliability: Instrument owned systems, define meaningful SLOs and data quality checks, and participate in a rotating on-call schedule.
Ingestion & Integration Jobs: Own and extend Python jobs running on Glue, Lambda, and Step Functions, primarily ingesting data from third-party APIs, with a smaller set pushing data out to downstream systems. Manage infrastructure in AWS CDK.
Mentorship & Technical Leadership: Pair with junior engineers, raise the bar on PR and architecture reviews, and define team patterns and standards. Bring a systems-thinking lens and clear communication to all conversations.