Backend Engineer, Data

at Stripe

📍 Canada
📍 Toronto, Canada

CAD 172,000-258,000 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.

Airflow Data Engineering Data Pipelines @ 4 Data Science @ 4 Debugging @ 4 Go @ 4 Hadoop @ 4 Hiring @ 6 Java @ 7 LLM Marketing @ 4 Python Reporting @ 4 SQL @ 7 Scala @ 7 Spark @ 4

Details

Stripe is a financial infrastructure platform for businesses. The Data Foundations team drives Data Engineering and Data Apps and Tooling work across Stripe, providing tools and infrastructure to move, store, process, and analyze data. The team is seeking data-minded software engineers to build data pipelines and data-driven user experiences and manage business-critical data used across the organization.

Responsibilities

Design, develop, and own data pipelines, models, and products that support Product, Data Science, and Go-to-Market functions.
Develop subject matter expertise and manage service-level agreements for data pipelines and full-stack web applications supporting critical stakeholders.
Build and refine Stripe's data foundations, including infrastructure, pipelines, and tools, using Scala, Spark, and Airflow.
Leverage LLMs and agents at scale to produce high-quality data for ambiguous problems.
Refine data marts that help the Go-to-Market organization forecast business performance and measure attainment toward targets.
Build data services that track key product metrics and measure the impact of strategies used by field teams.
Work across Spark, Scala, Java, SQL, and Python.
Collaborate with Product, Data Science, and Go-to-Market teams to solve data needs and improve data quality and customer experiences.

Requirements

Minimum Requirements

Six or more years of experience in software engineering, focused on building and maintaining data services or data-intensive applications.
Strong engineering background and an interest in data.
Experience writing and debugging data pipelines using a distributed data framework such as Spark, Hadoop, or Pig.
Inquisitiveness and ability to investigate data inconsistencies, identify issues, and resolve underlying data quality problems.
Knowledge of a backend development language such as Scala, Java, or Go, together with strong SQL experience.
Ability to communicate cross-functionally, derive requirements, and architect shared datasets.

Preferred Qualifications

Experience creating and maintaining data marts for business reporting.
Experience working with Product or Go-to-Market teams, including Sales and Marketing.

Team matching for a specific subteam begins during the final stages of the hiring process. Candidates may also be considered for other organizations based on experience and location.