Staff Data Engineer - Risk & Product Analytics

Nubeero Limited; Toronto; On-site; CAD 140,000 - 180,000; Full time

Job summary

Stripe is seeking a Staff Data Engineer in Toronto to lead a team responsible for building and operating scalable data pipelines and data warehouses. You will collaborate across product and risk teams to enable analytics, ML capabilities, and data-driven decision making across Stripe’s platforms.

The role emphasizes autonomy, a strong backend/data engineering background, and hands-on experience with distributed data tools, while promoting inclusive, high-quality engineering practices.

Qualifications

• This is a Staff-level role — that typically means 10+ years of experience building and operating data systems, pipelines, warehouses, infrastructure, and leading teams to deliver exceptional solutions
• A strong engineering background and passion for data as well as prior experience with writing and debugging data pipelines using a distributed data framework
• An inquisitive nature in diving into data inconsistencies to pinpoint issues, and resolve deep rooted data quality issues
• Knowledge of a backend development language (such as Scala, Java, or Go) and strong SQL experience
• Extreme customer focus, with a commitment to partnering with product, leaders across the business, and other Stripe engineers to understand their use cases
• Effective cross-functional collaboration, with the ability to think rigorously, communicate clearly, and make or coordinate difficult decisions and trade-offs
• Thrive with high autonomy and responsibility in an ambiguous environment
• Ability to foster and work in a healthy, inclusive, challenging, and supportive work environment
• Our stack is made up of Iceberg, Kafka, Change Data Capture, Flink, Spark, Airflow, Hive Metastore, Pinot, Trino, and AWS Cloud - experience with all or some of these tools is a huge plus
• Influencing open-source contributions
• Experience creating and maintaining data marts / warehouses to power business reporting needs
• Experience collaborating with Product, Go-To-Market, or Sales / Marketing teams
• Genuine enjoyment of innovation and a deep interest in understanding how things work, with the ability to question and direct architectural decisions
• Strong written and verbal communication skills for various audiences, including leadership, users, and company-wide

Responsibilities

• Lead the technical outcomes for a team of ambitious, talented engineers, providing mentorship, guidance, and support to ensure their success
• Partner with our recruiting team to attract and hire top talent
• Deliver cutting-edge data pipelines that scale to users' needs, focusing on reliability and efficiency
• Develop strong subject matter expertise and manage the SLAs of data pipelines and full stack web applications that support critical stakeholders
• Collaborate with product managers and peers across the company to create/improve canonical datasets and data warehouses, use golden paths, and ensure Stripes and customers are using trustworthy data
• Leverage AI/LLM and Agents at scale to produce and analyze high-quality data on ambiguous problems
• Have the opportunity to drive the execution of key data initiatives for Stripe, overseeing the entire development lifecycle from planning to delivery while maintaining high standards of quality and timely completion
• Foster a collaborative and inclusive work environment, promoting innovation, knowledge sharing, and continuous improvement within the team

Skills

Staff-level role

Data engineering

Strong SQL

Autonomy

Cross-functional collaboration

Tools

Airflow

Spark

Kafka

Flink

Hive Metastore

Pinot

Trino

AWS Cloud

Iceberg

Job description

Stripe is seeking a Staff Data Engineer in Toronto to lead a team responsible for building and operating scalable data pipelines and data warehouses. You will collaborate across product and risk teams to enable analytics, ML capabilities, and data-driven decision making across Stripe’s platforms.

The role emphasizes autonomy, a strong backend/data engineering background, and hands-on experience with distributed data tools, while promoting inclusive, high-quality engineering practices.