Job Overview
Location
Remote
Job Type
Full-time
Category
Data Engineer
Date Posted
February 24, 2026
Full Job Description
đź“‹ Description
- • Affirm is at the forefront of revolutionizing credit, offering consumers a transparent and flexible way to "buy now and pay later" without the burden of hidden fees or compounding interest. We are seeking a highly skilled and action-oriented Analytics Lead to join our dynamic People Analytics organization. This pivotal role is designed to drive the next evolution of our data infrastructure, modeling, and reporting capabilities, ensuring that Affirm has the most robust and insightful people data to power its growth and talent strategies.
- • The People Analytics team is the backbone of key talent programs across Affirm, including Talent Acquisition, Total Rewards, Feedback & Development, and core employee data management. Our mission is to build and maintain the foundational data assets that enable informed decision-making, foster a positive employee experience, and support strategic HR initiatives. As the technical lead for our people data ecosystem, you will be instrumental in shaping its future, designing scalable architecture, establishing rigorous engineering standards, and partnering across the business to deliver high-value, trusted data products.
- • The ideal candidate possesses a strong blend of analytical engineering expertise, a strategic vision, an insatiable curiosity, and the ability to lead and influence effectively within a collaborative environment. You will play a key role in defining our data roadmap, championing the adoption of modern tooling and automation, elevating our engineering best practices, and mentoring fellow analytics engineers. This is an exceptional opportunity to contribute to building the next generation of People Analytics at Affirm, directly impacting how we understand and develop our most valuable asset: our people.
- • Your responsibilities will encompass the end-to-end design and delivery of robust data solutions. This includes architecting and implementing both relational and non-relational database models, building efficient data pipelines, and developing insightful reporting and visualization solutions. You will guide all phases of the analytics development lifecycle (ADLC), from initial requirements gathering and meticulous design to development, rigorous testing, and seamless deployment.
- • A core aspect of this role involves developing, maintaining, and scaling sophisticated ETL/ELT pipelines that integrate data from various HR sources, such as Workday, Greenhouse Recruiting, and internal tools like Arbor. Ensuring the reliability, optimal performance, and future extensibility of these pipelines will be paramount.
- • You will be responsible for architecting and implementing scalable data models that are specifically optimized for analytical querying and designed for long-term maintainability, ensuring that our data is not only accessible but also structured for deep insights.
- • Maintaining the highest standards of data quality, integrity, and reliability across all our data assets is critical. This includes proactively introducing automation and implementing best practices for monitoring and validation to ensure our data is trustworthy and accurate.
- • Close collaboration with People Analytics stakeholders will be essential. You will translate complex business requirements into sound technical solutions and actively influence longer-term data architecture decisions, ensuring alignment with strategic objectives.
- • You will manage and optimize our cloud data warehouse infrastructure, likely utilizing platforms like Snowflake. This includes performance tuning, diligent cost management, and establishing secure access patterns to protect sensitive employee data.
- • A forward-thinking approach is encouraged, including leveraging AI and LLMs to automate data quality checks, enhance metadata management, and unlock deeper insights from unstructured HR data.
- • You will be a champion for engineering excellence, staying current with the latest technology best practices and advocating for their adoption across the People Analytics team.
- • Finally, you will own and manage critical aspects of data governance, security, privacy, and retention standards across all People Analytics systems, ensuring compliance and responsible data stewardship.
🎯 Requirements
- • 5+ years of demonstrated expertise with dbt (Data Build Tool), SQL, and Python, including experience writing clean, computationally efficient code for ETL processes and data manipulation, designing and building efficient, analytics-ready data models in dbt, and comfort with production-level IDEs (e.g., Cursor, Visual Studio) and Version Control (e.g., git, specifically GitHub).
- • Proven experience with cloud data warehouses (e.g., Snowflake), modern BI platforms (e.g., Sigma), data integration platforms (e.g., Fivetran), and orchestration platforms (e.g., Airflow).
- • Strong sense of ownership, intellectual curiosity, and the ability to think creatively and critically in a dynamic, fast-paced, and ambiguous environment.
- • Demonstrated ability to provide technical leadership, influence cross-functional partners, and mentor other team members.
🏖️ Benefits
- • 100% subsidized medical coverage, dental, and vision for you and your dependents.
- • Generous monthly stipends for technology, food, various lifestyle needs, and family-forming expenses.
- • Competitive vacation and holiday schedules designed to help you rest and recharge.
- • Employee Stock Purchase Plan (ESPP) enabling you to buy shares of Affirm at a discount.
Skills & Technologies
Python
GitHub
Git
REST
Pandas
Full Stack
Senior
Remote
$180k-230k
About Affirm Holdings, Inc.
Affirm Holdings operates a point-of-sale consumer lending platform that integrates with online and in-store checkout systems. Through its technology, shoppers can split purchases into fixed, transparent installment payments, while merchants gain conversion and larger order values. The company underwrites and services loans in the United States and Canada using alternative credit models and data partnerships with banks, avoiding deferred-interest structures. It earns revenue from merchant fees and interest on loans, and offers a mobile app for consumers to manage repayment schedules and access additional credit lines.
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