Beamery's unique jobs, skills and tasks data platform helps organizations navigate challenges and make more informed decisions across Talent Lifecycle Management.
Solutions power recruitment, mobility, upskilling, diversity, work architecture and workforce planning.
Deepening native integrations with SAP, Workday, Microsoft, and LinkedIn.
Embedding agentic AI to help customers plan smarter.
Advancing use of proprietary LLMs and knowledge graph technology.
Responsibilities
Own the technical strategy for the data platform: define the multi-quarter architectural vision, build the case for investment with EPD leadership, and sequence delivery incrementally.
Architect the analytics, semantic, and AI data layers: define how core business metrics, feature usage signals, and skills and task intelligence data are modelled, governed, and exposed as trustworthy products to BI, self-service, and AI consumers.
Set standards that scale beyond your team: establish architectural patterns, modelling conventions, and data contracts other teams adopt.
Grow senior engineers into stronger technical decision-makers through design review, pairing, and mentorship.
Own reliability and trust at the platform level: set approach to observability, data quality, SLAs, and incident response; lead RCAs on serious failures and drive systemic fixes.
Requirements
Staff-level data engineer who has built and run data platforms for internal and external consumers.
Experience with data warehousing, data lakes, and both real-time and batch processing.
Made hard tradeoffs in distributed, multi-tenant systems.
Comfortable in production: tracing upstream failures and making sound calls under pressure.
Led initiatives across team boundaries without formal authority.
Made architectural decisions others built on for years.
Work with minimal direction, explain tradeoffs and risks credibly to executive audiences.
Data transformations using dbt, including patterns for large, complex projects.
Data storage (SQL / NoSQL): schema design, modelling at scale, and multi-tenant isolation.
Back-end engineering: Python (nice to have: Typescript/Node.JS).
Pipelines: streaming, CDC, correctness, replayability, and evolution over time.
Data quality and observability: validation, testing, data contracts, monitoring, alerting, and incident response.
Infrastructure as code and containerisation (Terraform, Kubernetes).