Staff Engineer

The job at a glance Join our team and you’ll be responsible for providing technical leadership for our Data Platform team, defining technical direction and driving architectural decisions across data, platform and infrastructure domains. Working in the Data Platform department you will build scalable, reliable and well-governed data platform capabilities that support analytics, AI and business-critical products, while collaborating closely with Engineering Managers, Product Managers and Enterprise Architects. This is so important to us. By joining our team, you will be playing a vital role as together we reimagine the labour market to make it work for everybody. Your responsibilities - Define and evolve the technical vision, roadmap and architecture of the Data Platform, driving decisions across data, platform and infrastructure domains - Establish engineering standards, best practices and reusable patterns while partnering with Enterprise Architecture and influencing technical direction - Drive engineering excellence by improving code quality, testing, observability, reliability and operational practices - Support end-to-end delivery by guiding teams through complex technical challenges, improving decision-making, and contributing to planning and risk management - Mentor engineers, grow senior technical capabilities and champion AI-assisted development and engineering automation Your skills and qualifications - Proven experience as a Staff Engineer, Principal Engineer or in an equivalent senior individual contributor leadership role - Strong background in data platforms, distributed systems and cloud-native architectures, with experience shaping technical direction across teams and stakeholders - Solid expertise in relevant technologies such as AWS, Terraform, Kafka/MSK, Airflow, Glue or Spark, and building scalable data solutions - Experience with modern data ecosystems including data lakes/lakehouse architectures, Iceberg or similar table formats, as well as batch and streaming processing - Knowledge of data quality, governance, cataloguing and observability tools (e.g. Datadog), with DBT or AI-assisted engineering practices as a plus