Balendra Gubrele

About Balendra Gubrele

Data Engineer specializing in designing scalable, production-grade data platforms that convert raw enterprise data into analytics-ready, high-performance data assets. Strong expertise in distributed data processing, large-scale ELT architectures, and cloud-native data engineering using Databricks, Apache Spark, SQL, Python, AWS, and Azure.Experienced in building batch and event-driven data pipelines that handle structured and semi-structured datasets across enterprise environments. Proven capability in architecting end-to-end workflows involving ingestion, transformation, data modeling, optimization, and analytics enablement. Strong understanding of partitioning strategies, incremental processing, and performance tuning techniques to improve processing efficiency by 35–70% while maintaining reliability and fault tolerance.Hands-on experience integrating enterprise systems such as SAP and ERP platforms into centralized data platforms, ensuring data consistency, lineage, and governance across reporting layers. Worked extensively with cloud services including AWS Glue, Redshift, Lambda, DynamoDB, S3, EventBridge, and Azure Data Lake to design scalable and maintainable data architectures.Reduced manual operational effort by 90% by automating database workflows, scheduling recurring jobs, implementing monitoring mechanisms, and strengthening SQL-based data quality frameworks. Experienced in graph modeling using Neo4j, supporting complex relationship-based analytics and downstream use cases.Strong foundation in data modeling (star schema, normalization strategies), batch processing frameworks, distributed computing principles, and building fault-tolerant pipelines capable of supporting enterprise analytics, compliance, and business intelligence systems.Focused on building scalable, efficient, and production-ready data platforms that enable organizations to make reliable, data-driven decisions at scale.

Experience

1. Data Engineer

   BSY Consulting Pvt Ltd

   Apr 2025 — Present

   Genealogy Trace Platform – Johnson & Johnson Enterprise-grade traceability platform enabling material, batch, and vendor lineage across ERP systems. Tech Stack – Databricks, Apache Spark, Azure (ADLS, ADF), Neo4j, SQL, Python, SAP- Built a large-scale genealogy model linking 200K+ parent-child relationships and developed a Neo4j graph (100K+ nodes, 350K+ relationships) for end-to-end traceability- Engineered scalable ingestion pipelines in Databricks, enabling 5× faster lineage traversal compared to traditional RDBMS systems- Optimized distributed Spark processing using partitioning, caching, and AQE, reducing pipeline runtime by 40% while ensuring production-grade data validation (350+ SIT/UAT cases). Enterprise Training Compliance & Analytics Platform – Johnson & JohnsonScalable compliance data platform supporting executive-level governance and reporting. Tech Stack – Databricks, Azure Data Lake (ADLS), Spark SQL, Python, ThoughtSpot- Designed and implemented distributed ELT pipelines integrating multi-source enterprise datasets across 100K+ records- Engineered CEO-to-employee hierarchical data models and rolling 30/45/60-day compliance logic, improving reporting efficiency by 70%- Delivered analytics-ready data models powering executive dashboards and increased enterprise compliance visibility by 40%.

Education

• IPS ACADEMY, Indore

  Bachelor of Technology - BTech, Computer Science