Benevity is hiring a Senior Staff Data Platform Engineer in Toronto!
Benevity is hiring a Senior Staff Data Platform Engineer in Toronto to act as the technical anchor for its data platform and lead the design of its data democratization strategy. The role combines hands-on engineering with design leadership, bridging architectural vision and production delivery. The engineer will help domain teams move data from source systems through streaming and batch pipelines, medallion lakehouse layers and semantic data products used for reporting. The position reports to the Director of Engineering and emphasizes scalable, governed, metadata-driven platform capabilities on AWS, with open table formats, cataloging, lineage, data contracts and automated quality controls. The ideal candidate brings deep data engineering experience, strong software design fundamentals and the ability to mentor others while advancing modern, AI-assisted engineering practices. Benevity offers a flexible hybrid working approach.
Key responsibilities
- Provide hands-on technical leadership across ingestion, transformation, lakehouse, catalog and semantic layers.
- Design platform capabilities that let domain engineering teams land sources, manage schema changes and define reporting data products with less bespoke work.
- Lead delivery of a medallion architecture on AWS using object storage, open table formats, governance tooling and dbt transformations.
- Make pipelines configuration- and metadata-driven so new attributes, tables and data products can be added declaratively and safely.
- Ensure datasets and pipelines are registered, documented, discoverable and automatically enriched with lineage and ownership in the metadata platform.
- Define versioned, tested data product and semantic layer contracts with ownership, quality expectations and deprecation paths.
- Apply AI-assisted, spec-driven development workflows and build internal tooling that helps domain teams scaffold pipelines, models, tests and documentation.
- Collaborate with architects, product managers and staff developers, participate in architecture reviews, mentor engineers and maintain technical documentation.
Key requirements
- Degree in computer science, computer engineering or equivalent professional experience.
- Eight or more years of scalable development experience, including substantial data platform and pipeline work, plus at least two years in technical leadership on platform modernization initiatives.
- Strong software design fundamentals, including domain-driven design, loosely coupled systems and event-driven services.
- Hands-on experience with streaming and batch data engineering, including Kafka, Debezium or Flink, schema evolution and delivery guarantees.
- Experience with lakehouse and medallion architectures on AWS using S3, Apache Iceberg, Glue and Lake Formation, plus dbt and orchestration such as Airflow.
- Knowledge of metadata, catalog and lineage platforms such as DataHub, metadata-as-code, data contracts and automated schema governance.
- Experience designing governed semantic layers and data products, with familiarity in Looker/LookML and asset-based orchestration.
- Cloud-native AWS experience; GCP familiarity with BigQuery and Looker is expected, Azure is a plus.
- Proficiency with agile delivery, infrastructure-as-code, DevSecOps, automated testing and CI/CD tools such as GitHub Actions or Jenkins.
- Experience with AI-led, agentic development workflows and internal developer tooling; relevant cloud certification is a nice-to-have.
- Seniority: Senior Staff
- Work mode: Hybrid
- Key skills: Data platform engineering, AWS, Apache Iceberg, dbt, Kafka, Flink, DataHub, Data contracts, Semantic layer, Data governance, Infrastructure-as-code, CI/CD, AI-assisted development
Want more jobs?
Join our mailing list to receive updates about new data science and Senior Staff Data Platform Engineer jobs.
