Staff Data Engineer, Product Risk & Data Platforms

Stripe; San Francisco (CA); Hybrid; USD 204,000 - 305,000; Full time

Benefits offered by this job

Equity

401(k)

Medical, dental, and vision benefits

Wellness stipend

Job summary

Stripe is seeking a Staff Data Engineer in San Francisco to lead data platform initiatives across Payments, Risk, and Product. You will build scalable data pipelines and maintain data warehouses, enabling analytics and ML capabilities for the business.

You will mentor engineers, partner with product teams, and drive AI/LLM-enabled data workflows with a focus on reliability, performance, and data quality. Hybrid work options are available.

Qualifications

• 10+ years of experience building and operating data systems and leading teams.
• Strong engineering background with data pipelines and distributed data frameworks.
• Ability to identify data quality issues and drive resolution.
• Backend language knowledge (e.g., Scala/Java/Go) and strong SQL.

Responsibilities

• Lead the technical outcomes for a team of engineers with mentorship and guidance.
• Deliver scalable data pipelines and maintain data warehouses for stakeholders.
• Collaborate with product managers to create canonical datasets and trustworthy data.
• Leverage AI/LLM and agents to produce high‑quality data on complex problems.
• Drive end‑to‑end data initiatives from planning to delivery with high quality.
• Foster an inclusive, collaborative, and high‑performing team environment.

Skills

Data pipelines

Data warehouses

Distributed data frameworks

Tools

Airflow

Spark

Kafka

Flink

Iceberg

Kafka (DS)

AWS

Job description

Stripe is seeking a Staff Data Engineer in San Francisco to lead data platform initiatives across Payments, Risk, and Product. You will build scalable data pipelines and maintain data warehouses, enabling analytics and ML capabilities for the business.

You will mentor engineers, partner with product teams, and drive AI/LLM-enabled data workflows with a focus on reliability, performance, and data quality. Hybrid work options are available.