Alberto Chong

AIRLINES SECTOR - Data Strategy & Architecture Design: Lead the design, roadmap, and implementation of data architecture strategy initiatives to support real-time event streaming data products and AI use cases to ensures the coherent organization of data across multiple platforms and its availability for key business initiatives. This involves creating blueprints for data management, governance systems to integrate, centralize and maintain the data from many data sources into Strimzi Kafka, Confluent Cloud and LakeHouse. - Data Products Development: Lead the design and support the implementation of large-scale data processing systems capable of handling the volume, velocity, and variety of data that needs to be analyzed and processed leveraging Confluent Cloud and Flink SQL. AI Development: • Support Azure OpenAI integration projects; • Design,implement and support complex automation workflows and automation projects; using n8n. • Building GenAI & LLMs solutions and prototypes into scalable, production-grade applications such assistants, agents, RAG apps with Azure Foundry, Azure Databricks Genie, N8N that demonstrate tangible business value • Collaborate closely with cross-functional teams to identify AI use cases and deliver end-to-end solutions - Innovation and Continuous Improvement: Act as an event streaming ambassador – Enable business units to effectively leverage event streaming platform by providing guidance adoption. Stay up-to-date with industry trends and new technologies. Continuously explore innovative solutions and enhancements to the existing data architecture to improve its scalability, reliability, and efficiency. Collaboration: Work closely with project managers, technical teams to integrate resources and drive the successful end-to-end delivery of data solutions as a service across various technologies and platforms. Define and guide the best practices for data streaming architecture.

FINANCIAL SERVICES SECTOR Project Description: Mainframe Offloading and Datalake Migration from on prem cloudera to Azure Databricks - Monitor Kafka and Databricks clusters and implement necessary adjustments to optimize performance - Keep up to date with platform and service roadmaps - Collaborating with Solution Architects in the design and assessment of solutions and software and data engineering team in the delivery of solutions and services - Identifying bottlenecks and inefficiencies in processes hindering performance or progress and proposing measurable and risk-managed - Reporting against KPIs - Identify and eliminate the root causes of quality issues - Handle incident management, troubleshooting, problems management and resolutions - Monitor and resolve data pipeline and data infrastructure performance issues - Escalate all risks and incidents with speed and clarity - Removing technical blockers for individuals and teams - Manage and maintain Confluent Kafka ecosystem, ensuring its availability and scalability - Lead a remote team of 7 data engineers - Optimize and design operations processes and monitor and improve the efficiency of the existing processes - Collaborate with the data governance teams to design rules and processes - Drive an amazing team culture of focus, fun, and constructive feedback Technologies: ◦ Configuration and monitoring of Confluent Platform Apache Kafka Stack (Zookeeper, KSQLDB, Kafka Connect, Schema Registry, Rest Proxy, Control Center) ◦ Design of topics, producers and consumers according best practices ◦ IBM Infosphere CDC Mainframe for DB2 to Kafka ◦ Monitoring tool: Dynatrace, Prometheus, Grafana, ELK stack ◦ ITSM ServiceNow, Jira and Confluence ◦ Azure Databricks Jobs, SQL Warehouse, Alerts ◦ Unity Catalog ◦ Delta lake medallion architecture ◦ Azure DataLake Gen2 ◦ BMC Control M Orchestration ◦ POC Installation,configuration Trino cluster for data Virtualization ◦ GitHub

FINANCIAL SERVICES SECTOR Project with emphasis on Lambda architecture end to end near real time ETL data pipeline from Mainframe Db2 to Kafka and to MongoDb to build powerful single view of 360 Bank Clients with different indicators of different commercial products, personal Accounts informations, cards, loans, insurance. Technologies: Installation, Configuration and monitoring Confluent Platform Apache Kafka Stack (Zookeeper, Brokers, KSQLDB, Kafka Connect, Schema Registry, Control Center) ◦ Design of topics , producers and consumers. ◦ IBM Infosphere CDC Mainframe for DB2 to Kafka ◦ Data Pipleines with Kafka Connect Sink(MongoDb) ◦ Data Pipelines Ksqldb Streams Transformations and send to MongoDb

IOT SECTOR: Project with emphasis on Kappa architecture end to end near real time ETL data pipeline to build modern fast OLAP and help clients to move from business intelligence to continuous intelligence and alerts to operations teams. Technologies: ◦ Confluent Apache Kafka Cloud ◦ Installation, configuration and monitoring of KSQLDB cluster ◦ Installation, configuration and monitoring of Kafka Connect cluster ◦ Development and support of Kafka on Confluent Cloud, integrations including, topics consumers and streaming applications. ◦ Kafka Connect Source(CDC Debezium, JDBC) ◦ Kafka Connect Sink(HTTP, BigQuery) ◦ Data Pipelines with Ksqldb Streams ◦ Microservice Spring Boot Kafka Producer ◦ Data Pipelines with Spark Structured Streaming(Pyspark) Jobs on Azure Databricks ◦ Ubuntu 18 TLS ◦ Design Azure DevOps Scrum Methodology(Boards, Sprints, Epics, Features and tasks, repos) ◦ Azure DevOps Pipelines CI/CD to deploy Kafka resources ◦ Ansible Playbooks ◦ Github