Data Architect

Role & responsibilities

• Design and own the enterprise data architecture: lakehouse structure, data flow, storage strategy, and governance model. • Build and maintain scalable data pipelines for batch and streaming workloads. • Define data governance standards: data cataloguing, lineage tracking, quality monitoring, and access control frameworks. • Design schemas and data models optimised for AI/ML consumption: feature stores, training data pipelines, label stores. • Architect real-time data processing systems for live learner signals, telemetry, and product analytics. • Evaluate and evolve the cloud data platform stack: Databricks, Snowflake, BigQuery, or equivalent. • Collaborate with ML teams on feature engineering infrastructure and training data reproducibility. • Establish data contracts and API-first data design patterns across consuming teams. • Drive performance optimisation, cost engineering, and reliability across the data platform. • Lead cross-functional data teams and manage stakeholder relationships across engineering, product, and business units. • Translate business requirements from skilling, education, and government contexts into scalable, pragmatic data architecture decisions. Must-Have Skills • Strong experience in designing and implementing data solutions and platforms • Ability to work across multiple technologies, databases, cloud platforms, and data tools • Strong understanding of data architecture, integration, data modelling, and data flow design; hands-on experience designing and maintaining big data systems • Experience leading cross-functional teams and driving technical solutions • Excellent problem-solving, stakeholder management, and communication skills; strong hands-on ETL experience including building, optimizing, and maintaining ETL pipelines at scale • Experience translating business requirements into scalable data solutions • Familiarity with skilling, education, or workforce development domains is preffered.