Rajnandini Kandikatla

Data Engineer with 2+ years of experience specializing in the Modern Data Stack (GCP, DBT, Airflow). Proven ability in building scalable ELT pipelines and optimizing data systems for performance and cost. Highly proficient in processing large-scale datasets with PySpark and automating workflows to reduce administrative effort and ensure production-grade reliability.

Engineer I - Onix

(2024-09)

• Led AWS Redshift to GCP BigQuery migration for large-scale datasets, ensuring consistency and minimal downtime.
• Redesigned ELT pipelines shifting transformations to BigQuery + DBT, improving scalability and execution time via macro refactoring.
• Orchestrated end-to-end high-volume data pipelines using Apache Airflow and PySpark on Dataproc.
• Implemented BigQuery partitioning/clustering and automated Airflow DAG monitoring, significantly reducing query costs and manual effort.
• Executed comprehensive data platform migration from Snowflake to Google BigQuery across Computation, Integration, and Consumption layers.
• Automated data load from source through dynamic DAGs using Cloud Run Airflow to manage dependencies, and migrated 1000+ DBT models.
• Designed medallion architecture ELT pipelines using BigQuery, DBT, and PySpark for distributed ETL workloads.
• Developed incremental DBT models and implemented SCD Type 2 tracking, optimizing models by resolving query bottlenecks.
• Overcame SQL dialect differences using custom DBT macros and ensured reporting reliability via rigorous automated data reconciliation tests.