Staff Software Engineer, Product Risk

at Stripe

📍 Canada
📍 Toronto, Canada
📍 South San Francisco, United States
📍 Seattle, United States

USD 224,000-336,000 per year

SENIOR

✅ Remote ✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI API AWS @ 4 Airflow @ 4 Communication @ 7 Data Engineering Data Pipelines @ 8 Debugging @ 4 Flink @ 4 Go @ 4 Hive @ 4 Java @ 4 Kafka @ 4 LLM Leadership @ 7 Machine Learning Marketing @ 4 Payments Reporting @ 4 SQL @ 7 Scala @ 4 Spark @ 4 Trino @ 4

Details

Stripe is a financial infrastructure platform for businesses. The Product and Risk Data Engineering team provides the data engineering layer for Payments, Risk, and Product. The team builds and maintains data warehouses and pipelines that support analytics, machine learning capabilities, agentic workflows, and merchant-facing data interfaces.

Responsibilities

Lead the technical outcomes for a team of engineers, providing mentorship, guidance, and support.
Partner with recruiting to attract and hire top talent.
Deliver reliable and efficient data pipelines that scale to user needs.
Develop subject matter expertise and manage SLAs for data pipelines and full-stack web applications supporting critical stakeholders.
Collaborate with product managers and engineers to create and improve canonical datasets and data warehouses.
Promote trustworthy data usage through best practices, golden paths, and event-driven data API modeling.
Leverage AI, LLMs, and agents at scale to produce and analyze high-quality data for ambiguous problems.
Drive key data initiatives across the full development lifecycle, from planning through delivery.
Foster a collaborative, inclusive, innovative, and supportive engineering environment.

Requirements

Minimum Requirements

Staff-level experience, typically including 10+ years building and operating data systems, pipelines, warehouses, and infrastructure, as well as leading teams.
Strong engineering background and passion for data.
Experience writing and debugging data pipelines using a distributed data framework.
Ability to investigate data inconsistencies and resolve deep-rooted data quality issues.
Knowledge of a backend development language such as Scala, Java, or Go.
Strong SQL experience.
Strong customer focus and ability to partner with product leaders, business stakeholders, and engineers.
Effective cross-functional collaboration, rigorous thinking, clear communication, and sound decision-making.
Ability to thrive with high autonomy and responsibility in an ambiguous environment.
Ability to foster and contribute to a healthy, inclusive, challenging, and supportive work environment.

Preferred Qualifications

Experience with Iceberg, Kafka, Change Data Capture, Flink, Spark, Airflow, Hive Metastore, Pinot, Trino, and AWS Cloud.
Experience influencing open-source contributions.
Experience creating and maintaining data marts and warehouses for business reporting.
Experience collaborating with Product, Go-To-Market, Sales, or Marketing teams.
Interest in innovation and understanding how systems work, with the ability to question and direct architectural decisions.
Strong written and verbal communication skills for leadership, users, and company-wide audiences.