Developed ETL processes using Scala and Python with Spark, creating archetypes and implementing them in live environments. Implemented data quality checks including null values, duplicates, data types, and ranges within ETL pipelines. Managed data pipelines across multiple environments (local, development, and production). Used DATIO tools for development environments, test mesh construction, new data flows, and schema validations. Applied quality and safety methodologies to prevent incidents in production environments. Executed HDFS commands for environment management. Created acceptance tests and unit tests prior to production deployments. Performed data transfers between homogeneous and heterogeneous sources using transformations, schemas, and adapters. Used SQL to filter, group, and aggregate data. Used AWS services to design, build, and orchestrate data pipelines for ingestion, transformation, and loading. Applied Git and Bitbucket for version control, branching strategies, and CI/CD. Developed ETL pipelines storing data in Parquet format with partitioning for performance optimization. Applied naming conventions, schemas, and metadata to ensure data traceability and lineage. Standardized data schemas to enforce banking quality and security protocols. Applied data normalization techniques to ensure data integrity and storage efficiency.

Developed ETL processes using Python, Pandas, and Matplotlib. Built data pipelines with Spark, Dataiku, and Alteryx. Used Microsoft Azure Databricks and Data Factory for ETL orchestration. Performed BI development and data modeling. Managed databases using SQL. Designed multi-layer data architecture (Raw, Bronze, Silver, Gold). Enforced schema registry protocols for data consistency.

Taught Swift programming for iOS to university students. Prepared learning material, exercises, and examples. Provided training in mobile application development.