M R Kuladeep

Project: FMCG Digital Sales & Operations Data Hub| Software Engineer – Data Engineering ( Jan 2025 - Dec 2025)

• Created a self-serve ingestion layer that consolidated data from emails, internal portals, Azure Blob Storage and Tableau assets, ingesting 200k+ records per day into a Delta Lake-based ADLS platform.
• Replaced manual email and portal downloads with Microsoft Graph API integrations and selector-driven automation, cutting data acquisition effort by ~70%.
• Enabled analytics on Tableau TWBX/Hyper files by extracting and transforming report data in Databricks, making it consumable outside Tableau.
• Applied incremental processing and medallion architecture (Bronze → Silver → Gold) to reduce reprocessing time by ~45% while improving data reliability.
• Stabilized production workflows through ADF orchestration, Logic App alerts and Azure DevOps CI/CD, maintaining 99%+ pipeline success rates.

Project: Real-Time Subscriber Data Processing Framework | Data Platform Engineer (Feb 2022 - Dec 2024)

• Owned the design and operation of large-scale CDC and event-driven pipelines, handling 10M+ daily records across Oracle databases, APIs and secure file feeds.
• Implemented a multi-hop Kafka architecture where raw Oracle GoldenGate streams were staged in Cassandra before being re-published to curated Kafka topics for downstream processing.
• Built a secure ingestion flow for encrypted SFTP files, using shell-based decryption and Kafka publishing, enabling near real-time Snowflake materialized views for analytics teams.
• Solved complex API JSON ingestion challenges by flattening deeply nested structures using recursive CTEs, cutting manual data preparation and schema fixes by ~60%.
• Designed and executed large-scale historical reprocessing via Airflow DAGs, safely replaying billions of records without disrupting live streaming workloads.
• Streamlined releases using containerized CI/CD pipelines (Jenkins, Docker, Kubernetes), reducing deployment cycles by ~40% and improving production stability.

Project: Network Usage & Revenue Data Pipelines | Azure Data Engineer(Aug 2021- Feb 2022)

• Designed and implemented end-to-end batch and streaming pipelines using Azure Data Factory, Azure Databricks, Kafka and Azure SQL processing 500k-1M+ records per day.
• Automated ingestion of zipped CSV files from SFTP, implemented incremental data loads from MySQL and built Kafka streaming pipelines with watermarking and stateful processing to handle late-arriving data.
• Developed a data quality validation framework (schema, null and business checks) and integrated Logic App-based alerts, reducing data defects by ~60% and pipeline resolution time by ~50%.
• Improved performance and reliability through optimized transformations, retry logic and monitoring, achieving 99.5%+ pipeline success rates and supporting T+1 / near real-time reporting SLAs.