Jul 2023 – Present · 2 yrs 11 mos

• ● Built and maintained scalable data infrastructure on 𝗔𝗪𝗦 (S3, Glue, Redshift) and 𝗔𝘇𝘂𝗿𝗲 (Data Factory, Databricks, Synapse) to support high‑volume ingestion, transformation, and ML workloads.
• ● Developed and automated ETL/ELT pipelines using 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗦𝗤𝗟, and 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 across APIs, databases, and streaming data, reducing manual effort by 60%.
• ● Optimized big data pipelines handling billions of records through partitioning, compression, and Spark tuning, improving performance by 45% and ensuring 98% data accuracy with robust quality frameworks.
• ● Enabled real‑time and batch analytics via 𝗞𝗮𝗳𝗸𝗮 and API integrations in AWS and Databricks, powering near real‑time insights and data‑driven decision‑making.
• ● Designed data models and transformations using dimensional and medallion architectures, increasing ML‑readiness and data discoverability for analytics teams.
• ● Implemented orchestration and monitoring with 𝗔𝗽𝗮𝗰𝗵𝗲 𝗔𝗶𝗿𝗳𝗹𝗼𝘄, 𝗗𝗕𝗧, and 𝗔𝗪𝗦 𝗦𝘁𝗲𝗽 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀, enhancing reliability and reducing pipeline downtime by 30%.
• ● Delivered analytics‑ready datasets integrated with 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 and 𝗧𝗮𝗯𝗹𝗲𝗮𝘂, accelerating reporting cycles and improving visibility into key business KPIs.
• ● Engineered and supported a 𝗟𝗮𝗸𝗲𝗵𝗼𝘂𝘀𝗲 architecture using 𝗗𝗲𝗹𝘁𝗮 𝗟𝗮𝗸𝗲, reducing data silos by 70% and enabling seamless analytics across teams.
• ● Strengthened data governance and cataloging using 𝗔𝗪𝗦 𝗚𝗹𝘂𝗲 and 𝗔𝘇𝘂𝗿𝗲 𝗣𝘂𝗿𝘃𝗶𝗲𝘄, ensuring compliance, traceable data lineage, and easier data discovery.
• ● Optimized cloud infrastructure with auto‑scaling, cost tuning, and monitoring, achieving up to 25% savings while maintaining high performance.
• ● Collaborated cross‑functionally with product, analytics, and ML teams to align pipelines with model training and experimentation, streamlining AI workflows and deployment cycles.

Jan 2023 – Jun 2023 · 5 mos

• ● Supported the data engineering team in building ETL pipelines and visualization solutions on 𝗔𝘇𝘂𝗿𝗲, gaining hands‑on experience with Azure Data Factory, PySpark, and analytics workflows.
• ● Assisted in designing and implementing ETL pipelines using 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗙𝗮𝗰𝘁𝗼𝗿𝘆 and 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 to ingest and transform data from multiple sources for reporting and analytics.
• ● Performed data preprocessing and cleaning to improve reliability and consistency across dashboards and reports used by business teams.
• ● Created interactive 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 dashboards to visualize KPIs, helping stakeholders track performance and make data‑driven decisions.
• ● Collaborated with senior engineers to learn and apply data modeling concepts, 𝗗𝗲𝗹𝘁𝗮 𝗟𝗮𝗸𝗲 architectures, and 𝗔𝘇𝘂𝗿𝗲 𝗦𝘆𝗻𝗮𝗽𝘀𝗲 for scalable querying.
• ● Contributed to initial pipeline setups, including schema mapping, validation rules, and transformation logic under mentor guidance.
• ● Gained exposure to data governance, metadata management, and data quality checks to ensure accurate and compliant datasets.
• ● Experimented with basic 𝗣𝘆𝗦𝗽𝗮𝗿𝗸 optimizations for handling larger datasets, strengthening understanding of distributed processing.
• ● Documented workflows, data flows, and process improvements to support knowledge transfer and future automation.