Ramya Dadi

Detail-oriented Data Engineer with hands-on experience building ETL pipelines, transforming and validating data, and delivering reporting solutions using SQL, Python, Snowflake, and Power BI. Currently upskilling in modern cloud data engineering — AWS, Apache Spark/PySpark, dbt, and Airflow — through structured projects, with a growing focus on AI/LLM and RAG pipelines. Seeking to apply modern data engineering practices to build reliable, scalable data solutions.

Data Engineer at Indo-American Institution (2013-01 – 2015-12)

• Developed and maintained ETL pipelines to collect, transform, validate, and load institutional data, processing ~10,000+ student records daily.
• Built complex SQL queries, stored procedures, and data transformations to support enrollment, course, faculty, and administrative reporting.
• Used Python for data processing, cleaning, validation, and automation of routine data tasks, reducing manual reporting effort.
• Integrated data from multiple sources into centralized relational databases, improving data accessibility for downstream analysts.
• Performed data quality checks to identify missing, duplicate, and inconsistent records, improving reporting accuracy.
• Created 15+ Power BI dashboards used by 50+ faculty and administrative staff to monitor enrollment and student performance trends.
• Worked with Hadoop/HDFS and Hive for processing larger datasets and learning distributed data systems.
• Assisted with database performance tuning and troubleshooting of ETL and SQL issues.
• Documented data processes, mappings, transformation rules, and reporting requirements for handoff and audit.
• Collaborated with business and academic users to gather requirements and deliver reliable datasets.

B.Tech in Computer Science Engineering – JNTU