Description
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.
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.
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
- 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
- Make pipelines configuration and metadata driven
- Ensure every pipeline, dataset and data product is registered, documented and discoverable in our metadata platform (DataHub)
- Design the data product and semantic layer contracts that reporting depends on
- Make AI a natural extension of your engineering practice
- Work with Principal Architects to lead the technical direction of our organizational strategy
- 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
- Provide mentorship and guidance to other developers on the team
- 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
- 8+ years of scalable development experience, with lead and software design accountabilities
- 2+ years in a technical leadership role on building scalable platforms
- Excellent communication skills
- Solid software design fundamentals
- Experience with domain driven design, loosely coupled systems, and event-driven, decoupled services
- A track record of building reusable abstractions and frameworks that other engineers can build on
- 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)
- Lakehouse and medallion architecture on open table formats (S3, Apache Iceberg, AWS Glue, Lake Formation)
- Warehouse engines at scale and declarative, asset-based orchestration (e.g. Airflow)
- Experience making data discoverable, governed and trustworthy as a platform capability
- 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
- Data quality, observability and service levels for pipelines and data products
- Experience with data products and the semantic layer
- Designing and modelling a semantic layer as a governed, reusable product
- 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
- Proficiency in agile, Infrastructure-as-Code, DevSecOps and automated testing, with CI/CD experience
- Experience with AI-led, spec-driven and agentic development workflows (e.g. Cursor), and building internal developer tooling that other engineers rely on
- Proven track record of building performant, scalable and cost effective products
- Commitment to continuous improvement in code, processes, and team development