• Design and build robust, reusable, parameter-driven ingestion and transformation pipeline using Azure Data Factory • Implement medallion architecture on Azure Data Lake Storage Gen2 • Build performant ELT workflows that leverage pushdown to source systems • Develop and optimize PySpark notebooks and jobs on Azure Databricks or Synapse Spark • Design dimensional models and data vault patterns for analytics consumption • Implement Slowly Changing Dimensions and Change Data Capture • Tune distributed SQL workloads in Synapse Dedicated SQL Pool • Implement CI/CD for data pipelines using Azure DevOps • Instrument pipelines with robust logging and monitoring • Lead or contribute to legacy-to-cloud migrations

• Architect event-driven pipelines (Kafka) and develop new data models that ensure transactional integrity (ACID) for commercial events like invoices, payments, and adjustments • Automate scalable ETL processes and refactor next-generation data architectures to improve quality, security, and coverage for rapidly growing business demands • Collaborate across teams to codify business processes into self-measuring systems, debugging complex challenges to ensure the reliability of financial operations

• Maintain and improve core data infrastructure for a key client account. • Architect and implement critical data foundations for advanced analytics and AI initiatives. • Hands-on development in SQL and Python within modern cloud environments. • Creating high-performance generation pipelines for product models.

• Design, develop, and maintain scalable data pipelines focused on ingestion via CDC (Change Data Capture) using Oracle GoldenGate; • Configure and manage real-time and near-real-time data replication between source systems and cloud environments; • Ensure data consistency, integrity, and synchronization between source and target systems; • Monitor ingestion pipelines, perform troubleshooting, and optimize CDC process performance; • Support full and incremental (delta) load strategies; • Develop and maintain data processing pipelines using Azure and Databricks (Spark); • Implement transformations following modern data architecture patterns using Bronze, Silver, and Gold layers; • Optimize pipelines for performance, scalability, and cost efficiency; • Work with structured and semi-structured data for analytical consumption, reporting, and AI/ML initiatives; • Collaborate with data architects to define modern Lakehouse architectures; • Support data governance, data catalog, lineage, and compliance initiatives; • Ensure data availability, reliability, security, and quality for downstream consumption.