Data Engineer with analytics

Built and led the analytics layer for customer feedback data, integrating Copilot frameworks and autonomous AI Agentic workflows to automate data discovery for business teams. • AI Agentic Workflows & Copilot Integration: Architected autonomous AI Agent workflows and integrated Copilot frameworks over unstructured customer feedback datasets, enabling natural language querying and automated data discovery for business teams. • Scalable Data Pipelines: Designed and deployed scalable ETL/ELT pipelines using Python and PySpark to ingest high-volume feedback and transactional datasets into GCP BigQuery. • Storage & Query Optimization: Optimized BigQuery partitioning, clustering, and storage models to handle high-frequency inventory and customer analytics, significantly reducing dashboard query latency. • Workflow Orchestration: Configured and managed complex DAGs using Apache Airflow in GCP to orchestrate daily batch processing, ensuring reliable, low-latency data availability for downstream analytics. • CI/CD & Pipeline Reliability: Automated multi-stage pipeline deployments using GitLab CI/CD, maintaining strict version control, data reproducibility, and automated monitoring.

• Travelled to India to support family during a personal emergency. • Maintained hands-on technical proficiency via self-directed projects focused on GCP data engineering, SQL query optimization, PySpark pipeline design, and Apache Airflow orchestration.

Media Data Delivery — Cloud data lake for a Dutch broadcaster, enabling real-time reporting and data-driven decisions. • System Design: Designed and implemented scalable data and analytics solutions in Azure and Databricks, translating complex business requirements into technical architecture. • Pipeline Engineering: Designed event-driven, real-time streaming pipelines to ingest structured and unstructured data into Azure Data Lake, leveraging PySpark to process millions of records. • Modelling & Performance: Developed reusable data models and pipelines using Databricks and dbt while optimizing system performance and reliability with Delta Lake and Databricks SQL. • Governance & Quality: Maintained data governance, security, and compliance standards in Azure while ensuring quality through CI/CD practices and monitoring via Apache Airflow. • Consulting & Collaboration: Provided technical consulting, project scoping, and effort estimation for stakeholders within international teams.

Banking Cloud Infrastructure — Modernizing on-premise banking systems with cloud-native Azure architecture. • Cloud Migration & Integration: Migrated on-premise infrastructure to Azure-native solutions, implementing ADLS Gen2 architecture and OAuth2 authentication to improve processing efficiency. • Business Logic Translation: Performed reverse engineering on complex Oracle PL/SQL procedures, translating legacy business logic into optimized Python (PySpark) and Databricks SQL code. • Orchestration & DevOps: Managed complex batch workflows using Apache Airflow and Azure Data Factory, while designing multi-stage CI/CD pipelines using GitHub Actions and Azure DevOps. • Transaction & Risk Analytics: Analysed high-volume transactional datasets to identify potential fraud patterns, anomaly indicators, and customer risk signals across cloud banking modules.

Tax Processing Automation — Cloud and data pipeline setup for government tax declaration processing • Data Pipeline Engineering: Built foundational Python and PySpark ETL pipelines to extract, clean, and transform structured tax data across multiple source systems. • Automation & Processing: Assisted in optimizing data preparation workflows for automated tax processing, reducing manual effort by 30%. • Reporting & Analytics: Developed interactive data visualizations and reporting dashboards using Plotly and Seaborn to communicate operational outputs to stakeholders

Banking Support System — Software development and operational data reporting for banking applications in Bahrain. • SQL & Schema Optimization: Architected complex Oracle PL/SQL stored procedures to automate weekly banking operational reports, reducing manual effort by 20% and improving reporting latency for regional stakeholders in Bahrain. • Data Architecture: Optimized Oracle PL/SQL views and relational models for banking data warehouse systems, reducing report generation time by 20%. • Reliability & SLA: Maintained 95% issue resolution within SLA timelines, ensuring operational continuity for mission-critical banking systems.