Kishore Padman

<p>Modernising Betfred’s legacy SQL Server data warehouse into a scalable AWS lakehouse supporting both batch and real-time analytics across multiple country operations.</p><p> Returned to the programme in Apr 2026 to drive its production deployment and go-live, taking the platform from build into live operation.</p><p> Designed and implemented a centralised S3 data lakehouse using Apache Iceberg, organised into a medallion (Bronze/Silver/Gold) architecture with schema evolution and partitioned storage, providing a single governed source of truth and efficient access to large volumes of historical data.</p><p> Engineered real-time Change Data Capture from SQL Server using AWS DMS, Debezium and Amazon MSK (Kafka), streaming transactional data with low latency to power near-real-time analytics.</p><p> Built scalable batch and transformation pipelines using AWS Glue and EMR (PySpark), delivering curated, analytics-ready data products into Amazon Redshift for analytics and reporting.</p><p> Developed real-time streaming pipelines using Apache Flink on AWS, enabling low-latency processing of transactional events for near-real-time analytics.</p><p> Designed and implemented a dbt transformation framework for the Betfred South Africa rollout, creating modular, version-controlled SQL models in Redshift.</p><p> Provisioned and managed cloud infrastructure as code using Terraform and containerised workloads with Docker, enabling repeatable, automated deployments across environments.</p><p> Designed a Common Data Model standardising core entities (Customer, Bet, Promotion) across multiple country operations, improving data consistency and cross-market reporting.</p><p> Implemented data governance and role-based security using AWS IAM, S3 bucket policies and Redshift permissions, ensuring controlled access to sensitive data and regulatory compliance.</p><p> Built reports and dashboards using Amazon QuickSight and Power BI Report Builder, enabling data-driven decision-making for business users.</p><p> Provided technical guidance to fellow engineers and partnered with stakeholders to prioritise high-impact deliverables.</p>

<p>Backend developer on Sanofi’s DICE e-commerce programme.</p><p> Designed and implemented Returns and Claims functionality integrated with Salesforce (data flows, APIs and error-handling).</p><p> Supported testing and stabilisation through to production deployment.</p>

<p>Modernising Betfred’s legacy SQL Server data warehouse into a scalable AWS lakehouse supporting both batch and real-time analytics across multiple country operations.</p><p> Returned to the programme in Apr 2026 to drive its production deployment and go-live, taking the platform from build into live operation.</p><p> Designed and implemented a centralised S3 data lakehouse using Apache Iceberg, organised into a medallion (Bronze/Silver/Gold) architecture with schema evolution and partitioned storage, providing a single governed source of truth and efficient access to large volumes of historical data.</p><p> Engineered real-time Change Data Capture from SQL Server using AWS DMS, Debezium and Amazon MSK (Kafka), streaming transactional data with low latency to power near-real-time analytics.</p><p> Built scalable batch and transformation pipelines using AWS Glue and EMR (PySpark), delivering curated, analytics-ready data products into Amazon Redshift for analytics and reporting.</p><p> Developed real-time streaming pipelines using Apache Flink on AWS, enabling low-latency processing of transactional events for near-real-time analytics.</p><p> Designed and implemented a dbt transformation framework for the Betfred South Africa rollout, creating modular, version-controlled SQL models in Redshift.</p><p> Provisioned and managed cloud infrastructure as code using Terraform and containerised workloads with Docker, enabling repeatable, automated deployments across environments.</p><p> Designed a Common Data Model standardising core entities (Customer, Bet, Promotion) across multiple country operations, improving data consistency and cross-market reporting.</p><p> Implemented data governance and role-based security using AWS IAM, S3 bucket policies and Redshift permissions, ensuring controlled access to sensitive data and regulatory compliance.</p><p> Built reports and dashboards using Amazon QuickSight and Power BI Report Builder, enabling data-driven decision-making for business users.</p><p> Provided technical guidance to fellow engineers and partnered with stakeholders to prioritise high-impact deliverables.</p>

<p>Delivered scalable batch and streaming data pipelines across multiple large-scale Vodafone transformation programmes (VBIT, CIAS, NEDS, WFM, Digital Twin), migrating enterprise workloads from Cloudera, Oracle, MySQL, SFTP, S3, NiFi and APIs to Google Cloud.</p><p> Designed and developed batch pipelines using Data Fusion and Dynamo to ingest data from REST APIs, Oracle and SFTP sources, apply complex transformations and load into BigQuery.</p><p> Built reusable Dynamo templates and custom Data Fusion plugins — checksum validation, GPG encryption/decryption, HashiCorp Vault connectivity, and SFTP and Amazon S3 integration — accelerating delivery and standardising pipelines across teams.</p><p> Engineered real-time streaming pipelines using Dataflow, Data Fusion and Pub/Sub, streaming data from APIs into Pub/Sub and delivering transformed data to downstream systems.</p><p> Implemented complex transformations, calculations, encryption/decryption and data pseudonymisation using the AEAD plugin to protect sensitive data.</p><p> Built Cloud Functions to process NiFi checksum and data files, replacing legacy Cloudera analytics, with post-processing orchestrated through Airflow DAGs.</p><p> Designed BigQuery and Cloud SQL schemas with partitioning and clustering for query performance and cost optimisation.</p><p> Developed Dataflow jobs to validate, transform and load data from Cloud Storage into BigQuery.</p><p> Orchestrated end-to-end workflows with Cloud Composer (Airflow) DAGs and built CI/CD pipelines in Jenkins.</p><p> Managed data governance and data-dictionary configuration in Collibra.</p><p> Provided deployment support, knowledge transfer and training to support teams.</p>

<p>Delivered enterprise Java and Spring solutions across transport, defence, retail and digital sectors.</p><p> Built multi-tier applications, microservices and integration layers (REST, SOAP, Kafka).</p><p> Implemented automated build and deployment using Maven, Jenkins and Docker.</p><p> Applied architectural rigour to current cloud data platform work.</p>