Staff Data Engineer, Product Risk & Analytics

United States Digital Space LLC; Toronto; On-site; CAD 140,000 - 180,000; Full time

Job summary

United States Digital Space LLC is seeking a Staff Data Engineer in Toronto to lead the data engineering team, build scalable data pipelines, and own end-to-end data infrastructure for payments, risk, and product analytics.

You will collaborate across product and engineering teams, drive data quality, and leverage modern technologies like Iceberg, Kafka, Flink, Spark, Airflow, and AWS to enable trusted data platforms for the company.

Qualifications

• 10+ years of experience building and operating data systems, pipelines, warehouses, and leading teams.
• Strong background in data engineering with distributed data frameworks.
• Ability to debug data quality issues and dive into data inconsistencies.
• Backend development experience (Scala/Java/Go) and strong SQL.
• Excellent collaboration with product and engineering teams.

Responsibilities

• Lead the technical outcomes for a team of engineers, providing mentorship and guidance.
• Partner with recruiting to attract and hire top talent.
• Deliver data pipelines that scale with reliability and efficiency.
• Develop expertise and manage SLAs for data pipelines and web applications.
• Collaborate with product managers to create canonical datasets and data warehouses.
• Leverage AI/LLM and Agents to analyze data on ambiguous problems.
• Drive key data initiatives from planning to delivery with high quality.

Skills

Backend language (Scala/Java/Go)

SQL

Data pipelines

Leadership

Autonomy

Tools

Iceberg

Kafka

Change Data Capture

Flink

Spark

Airflow

Hive Metastore

Pinot

Trino

AWS Cloud

Job description

United States Digital Space LLC is seeking a Staff Data Engineer in Toronto to lead the data engineering team, build scalable data pipelines, and own end-to-end data infrastructure for payments, risk, and product analytics.

You will collaborate across product and engineering teams, drive data quality, and leverage modern technologies like Iceberg, Kafka, Flink, Spark, Airflow, and AWS to enable trusted data platforms for the company.