Mallikarjun S Guluguli

Accomplished Data Engineer with 3+ years building scalable batch/real-time ETL/ELT pipelines transforming large datasets into reliable data assets for analytics, ML, KPIs/metrics.

Expertise: Spark, PySpark, Hive, Airflow, Kafka, BigQuery; data warehousing, modeling, quality, SLAs, observability.

At PharmEasy: Led workflows, optimizations, migrations (Redshift >Hive>BigQuery) yielding 99%+ accuracy, 30-50% cost savings; cross-functional collaboration.

Data Engineer at PharmEasy (2023-08 – Present)

Healthcare E-Commerce company. Led workflows, optimizations, migrations yielding 99%+ accuracy and 30-50% cost savings with cross-functional collaboration.

• Engineered scalable ETL/ELT pipelines on GCP BigQuery processing 1M+ daily records; improved pipeline accuracy from <90% to 99%+
• Led end-to-end cloud migration from AWS Redshift to Hive to GCP BigQuery, reducing storage and compute costs by 30–50% while improving scalability
• Orchestrated Airflow DAGs with optimized scheduling and dependency management, significantly reducing peak-time SLA failures
• Automated reporting workflows using Airflow and Python, cutting turnaround time from 1 week to 1 day
• Designed reconciliation and validation frameworks using, reducing inventory discrepancies from 5–6% to <0.5%
• Built RCA frameworks using PySpark to diagnose and resolve production pipeline failures, improving issue resolution speed
• Maintained 99%+ data accuracy SLAs across large-scale inventory and financial datasets
• Developed scalable analytical data models and layered BI architecture, improving data consistency and reliability
• Built supply chain visibility pipelines tracking invoice and shipment lifecycles for proactive monitoring
• Created self-serve tools (Item Ledger, Recon Tool) enabling real-time analytics and reducing analyst dependency
• Delivered inventory aging and working capital analytics supporting finance and SCM decision-making
• Optimized SQL queries and cluster utilization, reducing execution time from 5 hours to 1–1.5 hours
• Applied partitioning and workload optimization strategies, achieving 30–50% cloud cost reduction
• Performed WMS analytics to identify bottlenecks in warehouse operations and improve efficiency
• Collaborated with cross-functional teams, defined data contracts, and authored BRDs and solution documents

B.E. in Electronics and Communication – Visvesvaraya Technological University (2019 – 2023)