Data Engineer
Backend-leaning data engineer at Class8, whose products help trucking companies run operations and connect with freight. You'll own MongoDB/PostgreSQL performance, ship Python/FastAPI services, and build batch and streaming data pipelines on AWS (Kafka, Flink, Airflow, Iceberg), with production on-call duties.
- Compensation
- CA$80K–CA$150K / yr
- Location
- Toronto
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 6+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 6+ years backend software engineering experience, with meaningful work on data-intensive production systems
- Strong Python and FastAPI (or similar async Python web frameworks) in production
- Deep hands-on experience with PostgreSQL and MongoDB: schemas, indexes, transactions, query optimization, migration strategies
- Solid SQL skills and comfort reasoning about access patterns, consistency, and performance tradeoffs
- Experience building REST APIs, services, and integrations
- Track record of shipping and owning backend systems in production
- BS/MS in Computer Science or a related field
- Passion for continuous improvement and a practical AI-first mindset
Nice to have
- Data pipelines and orchestration
- Apache Kafka (or Kinesis/SQS-heavy event architectures) and stream processing concepts
- Apache Flink or comparable stream/batch processing frameworks
- Apache Iceberg or similar open table formats on object storage / data lake workloads
- Databricks (Spark, lakehouse patterns, or comparable managed analytics platforms)
- Docker, CI/CD, and infrastructure-as-code exposure on AWS
- Familiarity with CDC, idempotent consumers, backpressure, and failure handling in distributed pipelines
Full description
Compensation: $80k – $150k CAD • No equity
Class8 is building a suite of products to help trucking companies better run their business, and help them connect with freight opportunities through brokerages and other data partners. Our team works with real-time, big data across thousands of trucks, and creates apps across multiple surface areas including mobile, desktop, and embedded vehicles.
This role is for a strong backend engineer who loves data: you'll ship and optimize production Python/FastAPI services, tune MongoDB and PostgreSQL for scale, and help us build and improve our data pipelines. You don't need to know every tool on day one - we care more about database depth, production ownership, and sound judgment on distributed, data-heavy systems.
In This Role, You Will:
- Own database performance: schema design, indexing, query optimization, migrations, and data modeling across MongoDB and PostgreSQL.
- Design, build, and maintain FastAPI backend services that power fleet dispatch, operational data, and product features.
- Improve application and data-path performance through profiling, observability, and iterative tuning—not one-off fixes.
- Build and maintain batch and streaming data workflows.
- Use an AI-first approach to development, leveraging AI to accelerate implementation while maintaining strong ownership, judgment, and understanding of the code produced.
- Participate in production operations—debugging, incident response, and hardening systems you build (including on-call as the team scales).
You’re Right For The Role If You Have These Skills:Required:
- 6+ years of backend software engineering experience, with meaningful work on data-intensive production systems.
- Strong Python and FastAPI (or similar async Python web frameworks) in production.
- Deep hands-on experience with PostgreSQL and MongoDB: schemas, indexes, transactions where relevant, query optimization, and migration strategies.
- Solid SQL skills and comfort reasoning about access patterns, consistency, and performance tradeoffs.
- Experience building REST APIs, services, and integrations.
- Track record of shipping and owning backend systems in production
- BS/MS in Computer Science or a related field
- Passion for continuous improvement and a practical AI-first mindset (accelerate implementation and workflows—reviews, tests, docs—without sacrificing quality or understanding).
Bonus If You Have:
- Data pipelines and orchestration.