<p>Acquire, ingest, and process data from multiple sources systems into Microsoft Azure platform.</p><p> Design and build data pipelines for machine learning that are robust, modular, scalable, deployable, reproducible, and versioned.</p><p> Design and develop ETL pipelines from data warehouse to ADF, Databricks and Snowflake.</p><p> Well experienced on technologies like Apache Spark, Pyspark, Databricks, and Azure Microsoft, SQL and orchestrating pipelines.</p><p> Designed and implemented scalable Data Lake and Medallion Architecture (Bronze, Silver, Gold) solutions to ingest, cleanse, transform, and curate large-volume data, enabling reliable, governed, and analytics-ready datasets for downstream reporting and business intelligence.</p><p> Designed and implemented Data Vault and dimensional data models, including Hubs, Links, Satellites, fact/dimension tables, surrogate keys, and SCD Type 1/Type 2, supporting scalable, auditable, and analytics-ready data platforms.</p><p> Developed detailed Source-to-Target Data Mappings defining transformation rules, business logic, data types, and validation requirements, ensuring accurate data integration and consistency across source and target systems.</p><p> Responsible for analyzing and developing the full life cycle of a project including implementation/execution</p><p> Handling adhoc assignments that are moderate- to highly- complex and multi-faceted, to be performed under management guidance.</p><p> Review test plans and scripts, facilitate user acceptance testing and test strategies with developers and testers.</p><p> Perform detail data analysis, including data profiling, system source analysis and auditing under strict tolerance levels.</p><p> Effectively communicate with concise insights and/or visualizations while partnering collaboratively with colleagues (onshore/offshore model) and senior leaders.</p>
