• Design and deliver scalable Oracle-based data solutions, including XMLType and schema-based structures. • Develop and maintain ETL processes and data pipelines for data integration, migration, and deployment. • Create and support Oracle PL/SQL packages, stored procedures, functions, and triggers. • Collaborate with stakeholders to define data requirements, design relational schemas, and optimize data flows. • Analyze and optimize Oracle SQL performance through query tuning, execution plan analysis, and indexing strategies. • Establish and enforce data governance, security, data quality, and validation standards. • Provide technical leadership, mentorship, and guidance on complex data engineering challenges. • Evaluate new Oracle features, ETL technologies, and data engineering best practices to support continuous improvement.

• Design and deliver end-to-end data platforms for analytics, BI, machine learning and AI-ready data products • Build and optimise scalable ETL/ELT pipelines with Databricks, Spark/PySpark, SQL and Python • Own data processing logic, transformations and platform performance for reliable, scalable solutions • Design lakehouse architectures such as Medallion and Delta Lake aligned with business needs • Improve pipelines, development practices and delivery processes • Collaborate with architects, analysts and stakeholders to turn requirements into effective solutions • Apply data quality, governance and security standards across the platform • Mentor engineers and share knowledge across the team • Lead or contribute to technical design discussions and influence architecture decisions for data platform solutions

• Lead the design and evolution of lakehouse-based data platforms using Databricks and modern cloud technologies • Define how data is ingested, processed, governed, and consumed across batch, streaming, and AI workloads • Own architecture decisions around Delta Lake, data modelling, data pipelines, and platform scalability • Act as a trusted advisor to customers, helping them modernize legacy data landscapes into cloud-native, Databricks-driven platforms • Drive architecture for end-to-end data solutions, from ingestion (batch/streaming) to analytics, ML, and AI use cases • Guide teams in building robust Spark-based pipelines and efficient data processing frameworks • Establish best practices for Data engineering using Spark / PySpark; Data governance with Unity Catalog; Data lifecycle and quality across the platform • Lead solution workshops, technical deep-dives, and architecture sessions with clients and internal teams • Support pre-sales and solution shaping, especially around Databricks-based architecture and delivery models • Continuously improve platform design by introducing automation, DevOps practices, and reusable architecture patterns • Stay up to date with Databricks innovations (DLT, Asset Bundles, Genie, ML/AI features) and bring them into real use cases

• Define and design scalable, secure, and future-proof data architectures aligned with business and IT strategies • Act as a trusted advisor to customers, translating business requirements into comprehensive, end-to-end data solutions • Take ownership of architectural decisions across data platforms, integration patterns, and data governance • Collaborate with internal and external stakeholders to ensure successful implementation of the architecture • Establish best practices, standards, and guidelines for data management, data modeling, and data integration • Evaluate and introduce new technologies, ensuring alignment with long-term architectural goals • Support and guide project teams by providing architectural expertise and oversight