Nanda Krishna

Data Engineer with 1.7 years of experience designing and developing production-grade ETL/ELT pipelines using Azure Databricks, Azure Data Factory, PySpark, SQL, and Delta Lake. Experienced in building scalable data platforms using Medallion Architecture (Bronze, Silver, Gold), implementing batch and incremental data processing, optimizing Spark workloads, and delivering analytics-ready datasets for business reporting.

Databricks Certified Data Engineer

Associate with hands-on experience in data modeling, performance tuning, data quality validation, and cloud-based data engineering solutions.

Data Engineer - Data Maxis - Hyderabad, Telangana, India

(2024-06 - 2026-01)

• Designed and maintained 3 production ETL/ELT pipelines processing 10–50 GB of daily batch data using Azure Data Factory, Azure Databricks, PySpark, and Delta Lake to deliver analytics-ready datasets for business reporting.
• Developed metadata-driven Azure Data Factory pipelines using Lookup, ForEach, Copy Activity, Web Activity, and Stored Procedure activities to ingest data from AWS S3 into ADLS Gen2 with parameterized, idempotent processing.
• Implemented watermark-based incremental loading using Delta Lake MERGE operations to support safe reprocessing, prevent duplicate records and maintain transactional consistency across Bronze and Silver layers.
• Designed dimensional data models with fact and dimension tables following Star Schema and Medallion Architecture principles for scalable analytics and reporting.
• Optimized Spark and Delta Lake workloads using partitioning, ZORDER, OPTIMIZE, broadcast joins, and caching, reducing query execution time by 25% while lowering compute costs.
• Implemented data quality validation, schema enforcement, and audit logging while monitoring production pipelines, resolving failures, and maintaining reliable batch processing.

Bachelor of Engineering - Electrical and Electronics Engineering - Avanthi Institute of Engineering and Technology (2020-07 - 2023-11)