Principal Software Engineer, Data Products

Fetch•Reston, VA

About The Position

Fetch is hiring a Principal-level individual contributor in its Data organization to build the software behind data products that help brands understand consumer purchases, reach relevant shoppers, and measure advertising’s incremental impact. The role will help turn early capabilities into products partners can evaluate, integrate, and use at scale, including audience creation, scoring and delivery workflows, measurement pipelines, and reusable services and APIs for partner integrations. The position combines hands-on software development with architectural ownership and technical leadership over a significant product or platform domain, from early exploration through production. The engineer will work with Engineering, Data Science, Product, and go-to-market teams to determine what to build, validate solutions with partners, and establish systems to support a growing business. Initial ownership will be agreed during the interview process based on business priorities and the candidate’s experience. This is a full-time role that may be based at a Fetch US office or remotely in the United States. It reports to the VP of Data.

Requirements

15+ years of software engineering experience, including setting technical strategy, leading architectural decisions across organizations, and driving initiatives with company-level impact.
Demonstrated ownership of a data-intensive product from an ambiguous problem or prototype through production launch and sustained customer or partner use, including the ability to explain architectural decisions, post-launch changes, and how reliability, adoption, and business value were measured.
A track record of building and operating production software, with direct ownership of consequential architectural decisions and delivery outcomes.
Experience building advertising platforms, audience products, measurement or attribution systems, demand-side platforms, or closely related data products.
Strong software engineering fundamentals, including system design, backend development, testing, and production operations.
Experience designing systems that process substantial volumes of data, with an understanding of data quality, performance, reliability, and operational tradeoffs.
Demonstrated technical leadership of complex initiatives involving multiple engineers or teams, including setting direction, guiding implementation, and resolving technical disagreements.
Ability to move work forward with incomplete requirements, form informed technical opinions, and adjust approach as new evidence emerges.
Strong product judgment and ability to connect technical decisions to partner needs, product adoption, and business outcomes.
Clear communication with technical and nontechnical stakeholders, including explaining tradeoffs and constructively challenging requirements.

Nice To Haves

Experience bringing machine learning models into production in partnership with Data Science.
Familiarity with audience expansion, lookalike modeling, or predictive systems used in advertising.
Knowledge of statistics, anomaly detection, or techniques for identifying and addressing data quality issues.
Experience integrating data products with advertising platforms or external partners.

Responsibilities

Own technical direction and delivery for a significant area of Fetch’s off-platform data business, from problem definition and proof of concept through production delivery and ongoing improvement.
Design, implement, and maintain the services, APIs, and processing workflows behind data products; build on shared ingestion, storage, orchestration, and trusted data assets with Core Data Platform.
Establish clear interfaces and ownership so product teams can extend capabilities without duplicating foundational infrastructure.
Partner with Product, Sales, and external partners to clarify requirements, test assumptions, and translate feedback into engineering decisions.
Balance delivery speed, scalability, reliability, and maintainability when making architectural tradeoffs.
Work with data scientists to productionize audience models and measurement methods through repeatable data preparation, scoring, evaluation, and delivery workflows, with versioning, monitoring, and recovery mechanisms.
Jointly define production acceptance criteria that balance scientific validity, partner requirements, runtime, and cost.
Build validation, monitoring, and operational safeguards to ensure accurate, dependable outputs and meet agreed partner service levels.
Align engineers and cross-functional partners on design decisions, resolve dependencies, and guide implementation across contributing teams.
Mentor engineers, provide design and code reviews, and establish patterns that help the team build and operate systems effectively.