Mehdi Pourhadi

– Engineered high-throughput data validation tooling, detecting schema- and row-level differences across large datasets at ~2M rows per minute using parallel processing. – Led automated data migration pipelines for legacy-to-modern application transitions, dynamically mapping schemas and enforcing end-to-end data integrity. – Architected and automated secure database backup pipelines, implementing encryption and compression to reduce storage footprint by ~38% while preserving performance and data integrity. – Established centralized database observability, deploying monitoring dashboards to enable proactive performance and reliability management, leveraging AI-assisted workflows to accelerate issue diagnosis and documentation. – Engineered ERP data integration layer by developing custom Business Central extensions, enabling structured and reliable data exchange between internal systems and external data consumers. – Standardized internal platform tooling by containerizing core engineering services with Docker Compose, including LDAP-integrated issue tracking, ensuring reproducible deployments and reducing onboarding overhead. – Designed and deployed a containerized on-premise file-sharing solution as a compliant alternative to public cloud storage, supporting secure internal data collaboration within infrastructure constraints.

– Owned and operated production data pipelines orchestrated via workflow automation, supporting core financial datasets including transactions, interest calculations, payment networks, and customer profiles. – Streamlined end-to-end ETL workflows to deliver analytics-ready datasets for internal reporting and external banking partners, improving data accessibility and consistency across stakeholders. – Engineered scalable data processing jobs using distributed computation to deliver curated datasets to third-party consumers, enabling secure data sharing and downstream analytics. – Designed and implemented regulatory reporting pipelines for Anti-Money Laundering (AML), including Suspicious Transaction Reports (STR), calculating rolling annual deposit turnover with fine-grained temporal windows to ensure compliance and audit accuracy.

– Led large-scale data migrations between enterprise databases and big data platforms, selecting optimized migration strategies to reduce transfer time by ~42%. – Executed large-scale Oracle to Hadoop data migrations, moving ~3B records (~1.5TB) using PySpark and optimized ingestion strategies, cutting migration time by ~37%. – Supported production big data platforms, operating Hive, HDFS, and distributed storage formats for analytics workloads. – Maintained data platform reliability, monitoring database growth trends and managing storage efficiency across large datasets.

– Operated and supported production backend services, owning deployment pipelines and runtime environments for Django-based systems. – Led database migration optimizations, reducing MySQL-to-PostgreSQL transfer time by ~23% through performance-focused ingestion design. – Engineered automation for infrastructure discovery and operational workflows, integrating network metadata and issue tracking to improve visibility and efficiency. – Automated testing and operational processes, increasing execution efficiency by ~26%.

– Optimized query execution for large-scale production databases, leveraging execution plan analysis to improve performance by up to ~66% across relational and document data stores. – Built and operated high-volume data ingestion and reporting pipelines, processing 500GB+ datasets across heterogeneous data sources. – Automated ETL and reporting workflows, reducing delivery time by ~22% and improving operational reliability. – Operated and monitored multi-terabyte NoSQL datasets, ensuring availability, performance, and capacity stability.

– Deployed and operated database replication pipelines for data warehouse environments, supporting near-real-time data movement and analytical workloads. – Designed and executed backup, restore, and recovery strategies for production databases, leveraging native tooling to ensure data durability and operational resilience. – Led database cloning and migration workflows, using auxiliary database duplication to reduce environment provisioning and migration time by up to ~58%. – Supported application and middleware database integrations, installing and configuring database and application components to enable reliable backend services. – Built log processing and search pipelines for operational monitoring, ingesting high-volume system logs and optimizing retrieval to support real-time observability.