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Senior Staff Data Platform EngineerBenevity · Calgary, Alberta
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Benevity

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Senior Staff Data Platform Engineer

🗓️ Posted 2026-09-25 Calgary, Alberta Fulltime Hybrid ICT
💰 Salary Not shared 📊 Market 10,833 CAD–15,000 CAD/mo

About the Company

MEET BENEVITY Benevity is the way the world does good, providing companies (and their employees) with technology to take social action on the issues they care about. Through giving, volunteering, grantmaking, employee resource groups and micro-actions, we help most of the Fortune 100 brands build better cultures and use their power for good. We're also one of the first B Corporations in Canada, meaning we're as committed to purpose as we are to profits. We have people working all over the world, including Canada, Spain, Switzerland, the United Kingdom, the United States and more Benevity is seeking a talented Senior Staff Data Platform Engineer, who has an extensive record of hands-on data engineering and data product development experience. This role, reporting to the Director of Engineering, plays a crucial part in shaping and executing on the technical strategy across our data platform, and in building it hands-on alongside the team, raising both velocity and quality as we go. Position Overview: The Senior Staff Data Platform Engineer will be the technical anchor for our data platform, and will lead the design of our data democratization strategy. You will bridge the gap between architectural vision and production-grade engineering, working hands-on alongside the team. This role requires a deep understanding of designing scalable, governed systems that power our ingestion pipelines, medallion lakehouse, semantic layer and the data products serving reporting, ensuring

Responsibilities

  • ● Hands-on technical leadership across the full data path
  • ● Design and build the platform capabilities that let domain engineering teams take data from source to reporting themselves, landing new sources and schema changes into the streaming consumption pipeline, promoting them through the medallion layers, and defining the data products that reporting consumes.
  • ● Work hands-on across the whole path: streaming and batch pipelines, transformation models, the lakehouse and catalog layer, and the semantic layer that defines the data products consumers depend on.
  • ● Lead the design and delivery of our medallion architecture on an AWS lakehouse, object storage with open table formats (S3, Apache Iceberg), cataloging and fine-grained governance (AWS Glue, Lake Formation), and transformation frameworks (dbt), while continuing to operate and evolve our current warehouse environments and planning a credible path between the two.
  • ● Make pipelines configuration and metadata driven, so that adding an attribute, a table or a new data product is a declarative change a domain developer can make safely, rather than bespoke code only our team can write.
  • ● Ensure every pipeline, dataset and data product is registered, documented and discoverable in our metadata platform (DataHub), with schema, lineage and ownership propagated automatically rather than maintained by hand.
  • ● Design the data product and semantic layer contracts that reporting depends on, versioned, tested, with explicit ownership, quality expectations and deprecation paths.
  • ● Make AI a natural extension of your engineering practice, using tools like Cursor in spec-driven, agentic workflows, and build the internal, AI-assisted developer tooling that helps domain teams scaffold pipeline configuration, models, contracts, tests and documentation from a specification, with quality and governance enforced automatically in CI rather than through review queues.
  • ● Work with Principal Architects to lead the technical direction of our organizational strategy through implementing robust, extendable, and reusable architecture patterns.
  • ● Collaborate with product managers, staff developers, and other stakeholders to translate business requirements into technical specifications and data product designs.
  • ● Ensure security best practice across the technology stack using cloud native solution design.
  • ● Create and maintain technical documentation, architecture decisions and implementation processes, and ideate and deliver Proof of Concepts to advance our technical and product direction.
  • ● Provide mentorship and guidance to other developers on the team, encouraging continuous learning and professional development, fostering a collaborative and inclusive team environment.
  • ● Actively participate in architectural reviews to ensure quality, consistency and adherence to the north star architecture.

Requirements

  • ● Degree in Computer Science, Computer Engineering or equivalent professional experience
  • ● + years of scalable development experience, with lead and software design accountabilities, including substantial time building data platforms and pipelines.
  • ● + years in a technical leadership role on building scalable platforms, technology transformation and modernization initiatives.
  • ● Excellent communication skills, balancing product and technical needs to deliver the best outcomes.
  • ● Solid software design fundamentals
  • ● A track record of building reusable abstractions and frameworks that other engineers can build on, with the judgement to know when not to over-engineer, and comfort working through ambiguous, abstract problems
  • ● Comfort with abstract problem-solving and ambiguous challenges
  • ● Deep, hands-on data engineering experience across streaming and batch
  • ● Stream processing and event streaming platforms (Debezium, Kafka, Flink), including schema evolution and delivery-guarantee trade-offs
  • ● Lakehouse and medallion architecture on open table formats (S3, Apache Iceberg, AWS Glue, Lake Formation), plus a transformation practice with dbt
  • ● Warehouse engines at scale and declarative, asset-based orchestration (e.g. Airflow)
  • ● Metadata, catalog and lineage platforms (DataHub, or equivalents) and metadata-as-code approaches
  • ● Data contracts and schema governance enforced automatically in CI and at runtime, not by documentation and review meetings
  • ● Data quality, observability and service levels for pipelines and data products
  • ● Designing and modelling a semantic layer as a governed, reusable product (Looker/LookML; familiarity with headless semantic layers such as the dbt Semantic Layer or Cube is an asset)
  • ● Defining data products with identified consumers, contracts, versioning and deprecation paths
  • ● Enough exposure to analytics and reporting delivery to understand how consumers actually use what you serve them
  • ● Cloud, delivery and AI-led engineering practice
  • ● Extensive experience with cloud-native infrastructure in AWS; working familiarity with GCP (BigQuery, Looker); Azure is a plus
  • ● Proficiency in agile, Infrastructure-as-Code, DevSecOps and automated testing, with CI/CD experience (e.g. GitHub Actions, Jenkins)