Haseeb Sarwary

•Built ETL pipelines in Python and Dagster for a private client on a short-term paid engagement. •Building portfolio pipelines with Snowflake, dbt, Airflow, Kafka, Delta Lake and Great Expectations, deployed with Docker and Kubernetes (GitHub linked above). •Preparing for the AWS Certified Data Engineer Associate, Databricks Certified Data Engineer Professional and CKA certifications.

•Built and maintained BigQuery SQL transformation pipelines with automated data quality checks (Cloud Functions), supplying consistent datasets to downstream compliance and reporting systems. •Prepared, transformed and validated legacy data for an Azure-to-GCP migration, working with infrastructure engineers to troubleshoot issues across the migration. •Cut a 6-hour monthly regulatory reporting process to under 10 minutes by automating it end-to-end in Python and Power Automate, freeing analyst capacity across two teams. •Built LLM-powered document processing that summarises compliance and reporting documents and extracts key information automatically, reducing manual review time. •Introduced CI/CD for data pipeline deployments with GitHub Actions, reducing integration failures across the data platform team. •Automated ETL extraction and processing workflows with Python and Blue Prism, removing error-prone manual steps from operational reporting.

•Designed and deployed production ELT pipelines with dbt and Apache Airflow, improving pipeline reliability and reducing failures in production. •Containerised data workflows with Docker, giving reproducible builds across dev and production. •Built and maintained CI/CD pipelines with Git and Jenkins, reducing deployment errors and shortening release cycles. •Used Terraform to provision data infrastructure as part of the team's move to infrastructure as code. •Prototyped new pipelines and ran exploratory analysis in Databricks.

•Delivered supply-chain analytics with Azure Data Factory and Databricks, supporting logistics forecasting that improved operational efficiency by 15%. •Processed large-scale logistics datasets with Apache Spark for supply-chain planning and capacity management. •Maintained data integrity across aviation logistics systems in PostgreSQL and MySQL, with automated backups and validation checks.

•Reduced query times by 40% by designing and implementing a centralised data warehouse on Azure Synapse Analytics and Snowflake. •Saved 20+ hours of manual work per week by automating e-commerce ETL pipelines with Apache Airflow. •Built Power BI dashboards that became the primary reporting layer for commercial decisions.

•Built scalable ETL pipelines with Python and Apache Spark on GCP, processing large datasets for BI and reporting. •Managed data storage and access on AWS S3 across enterprise systems. •Achieved a 95% resolution rate on data and systems issues, using Python-based transformation and validation to fix data accuracy problems.