Senior Manager - Data EngineeringLove what you do and leave your markHybrid ways of working - Sydney or Melbourne locationEnterprise Ecosystem Impact: Architect the "paved road" delivery ecosystem, translating conceptual data architecture into scalable, production-grade pipelines.Generative AI Innovation: Spearhead the pragmatic rollout of Generative and Agentic AI tools to elevate engineering workflows, documentation, and code validation.As the Senior Manager - Data Engineering, you will serve as the primary IP Guardian for core data engineering standards, platform integrations, and high-performance data pipelines across our enterprise. Operating under a modern System-Builder Paradigm, this role moves beyond traditional line management to architect and scale our delivery ecosystem. In close collaboration with Data Architects, you will translate architectural blueprints into robust, production-ready engineering standards, pipeline templates, and automated delivery guardrails. Your core delivery philosophy is enablement: building self-service systems and establishing a "paved road" so that the right way to build is also the fastest, simplest path of least resistance for both internal squads and external partners. With a strong bias for action, you will champion incremental delivery over theoretical design, lead an internal cohort of 8–10 data engineers, and govern multi-million-dollar partner delivery across modern cloud and data ecosystems.Sounds good? Read on.Here is a taster of what you can expect in this role:System Delivery Enablement: Architect and maintain the "paved road" for data engineering by building self-service pipeline templates, automated CI/CD checks, and dbt testing suites that remove friction for delivery squads.Engineering IP Guardianship: Act as the custodian of backend data assets, operationalising blueprints into reusable pipeline patterns and certifying Gold-layer models and enterprise data registries via Informatica.People Capability Leadership: Directly guide, mentor, and develop an internal cohort of 8–10 data engineers, stepping back from day-to-day coding to foster technical mastery and team capability at scale.Partner Vendor Governance: Embed rigorous technical acceptance criteria, runtime efficiency targets, and milestone gates into fixed-outcome Statements of Work (SOWs), holding external partners to the "Self-Onboarding Standard".Data Reliability Standards: Establish enterprise-grade observability, data quality monitoring, and operational alerting frameworks to ensure Gold-layer datasets consistently meet downstream consumer SLAs.Cloud Compute Efficiency: Partner with enterprise FinOps functions to implement automated cost and resource guardrails across BigQuery, cloud storage, and orchestration tooling.Pragmatic Delivery Momentum: Champion continuous value delivery and iterative execution, making steady progress on active initiatives while systematically retiring legacy technical debt.Modern Workflow Automation: Drive the practical adoption of Generative and Agentic AI tooling across backend engineering workflows to accelerate testing and streamline technical documentation.
