Senior Data Engineering Lead — Scalable Pipelines & AI

Stripe; San Francisco (CA); Hybrid; CAD 283,000 - 424,000; Full time

Job summary

Stripe is seeking a Staff-level data platform engineer for a hybrid/hybrid-remote role. You will lead a team of engineers, deliver scalable data pipelines, and partner with product teams to produce trustworthy data assets.

The role emphasizes data quality, governance, and cross-functional collaboration. The ideal candidate has 10+ years in data systems, strong backend skills, and experience with Spark, Kafka, Flink, and AWS.

Qualifications

• 10+ years of experience building and operating data systems, pipelines, warehouses, and infrastructure.
• Strong engineering background with distributed data pipelines.
• Ability to resolve data quality issues and data inconsistencies.
• Backend development experience (Scala, Java, or Go) and strong SQL.

Responsibilities

• Lead the technical outcomes for a team of engineers, providing mentorship and guidance.
• Partner with recruiting to attract and hire top talent.
• Deliver scalable data pipelines with high reliability and efficiency.
• Develop expert knowledge and manage SLAs for data pipelines and full-stack apps.
• Collaborate with product and cross-functional teams to create canonical datasets and data warehouses.
• Leverage AI/LLM and agents to analyze data and unlock capabilities.
• Drive execution of key data initiatives from planning to delivery.
• Foster an inclusive and innovative team environment.

Skills

Leadership

Data engineering

Mentorship

Cross-functional collaboration

SQL

Backend development

Airflow

Spark

Kafka

Flink

Iceberg

AWS

Data warehousing

Change Data Capture

Tools

Airflow

Spark

Kafka

Flink

Iceberg

Hive Metastore

Pinot

Trino

AWS

Job description

Stripe is seeking a Staff-level data platform engineer for a hybrid/hybrid-remote role. You will lead a team of engineers, deliver scalable data pipelines, and partner with product teams to produce trustworthy data assets.

The role emphasizes data quality, governance, and cross-functional collaboration. The ideal candidate has 10+ years in data systems, strong backend skills, and experience with Spark, Kafka, Flink, and AWS.