Senior AI Data Engineer

Be a Part of Something BIG!  • Responsible for designing, building, and operating scalable data ingestion, transformation, and serving capabilities across a modern hybrid cloud data platform, ensuring solutions are reliable, secure, reusable, and aligned to enterprise architecture and governance standards. • Develop and optimise batch and streaming pipelines using cloud tools such as Databricks and Kafka, applying sound engineering practices to ensure performance, resilience, and maintainability. • Contribute to the delivery of reliable, secure, and high-quality data for analytics, reporting, and machine learning use cases • Lead the implementation and operationalisation of knowledge base and retrieval-augmented generation solution stacks to support scalable GenAI and agentic use cases across business domains Make an Impact by: • Design, build, optimise, and maintain batch and streaming data ingestion pipelines using platforms such as Databricks and Kafka, ensuring scalability, reliability, observability, and alignment with enterprise data architecture standards. • Perform data transformation and cleansing using PySpark or SQL based on business and technical requirements • Monitor and troubleshoot data workflows to ensure data quality and pipeline reliability • Provide technical guidance to engineers and delivery partners on data platform patterns, reusable components, code quality, deployment readiness, and production support practices. • Lead integration of data from diverse source systems including files, APIs, databases, and streaming platforms, working with source-system owners and consuming teams to define fit-for-purpose ingestion patterns and delivery timelines. • Help maintain metadata and pipeline documentation for transparency and traceability • Own production readiness for assigned data and AI platform components, including observability, incident triage, root-cause analysis, release coordination, and continuous improvement of operational runbooks. • Participate in integrating pipelines with tools such as Microsoft Fabric, Databricks, Delta Lake, and other platform components • Build and maintain knowledge base and RAG solution on variety of hosting platforms • Implement and operate knowledge base storage, lifecycle management and embedding/vectorization • Contribute to automation efforts using version control and CI/CD workflows • Apply data governance, security, access control, and operational risk policies during solution design and implementation, ensuring pipelines and knowledge platforms meet enterprise compliance requirements. Skills for Success:  • Bachelor’s degree in Computer Science, Engineering, or a related field • 5–8 years of experience in data engineering, data platform engineering, or cloud-scale analytics solution delivery, with demonstrated ownership of production pipelines and platform components. • Proven ability to independently design, build, optimise, and operate production-grade batch or streaming data pipelines, including orchestration, observability, error handling, performance tuning, and operational support. • Hands-on experience with Python and SQL for data transformation and validation • Familiarity with Apache Spark (especially PySpark) and large-scale data processing concepts • Experience with implementing knowledge base and RAG solutions for agentic AI use cases • Self-starter with strong problem-solving skills and a keen attention to detail • Able to work independently and lead technical discussions with engineers, architects, product owners, source-system teams, and business stakeholders to translate requirements into secure and maintainable platform solutions. • Strong documentation and communication skills • Strong understanding of enterprise data architecture, cloud security, access control, CI/CD, release management, and production operations for data and AI platform solutions.