Manoj G

At LSEG, I optimise large-scale forecasting-data pipelines for commodities trading and risk analytics, reducing historical ingestion time by 30%. I also developed reusable Python frameworks that transform GRIB weather files into structured NetCDF datasets for analytics and machine-learning use cases.

Previously at Tiger Analytics, I built a centralised insurance Lakehouse platform using Azure Data Factory, Azure Databricks, PySpark and ADLS Gen2. I delivered governed Bronze, Silver and Gold Delta Lake layers, metadata-driven ingestion, SCD Type 2 history, Kimball-style models and ML-ready datasets for fraud, churn and predictive analytics.

I’ve also built real-time financial transaction pipelines with Apache Kafka, PySpark, Spark Structured Streaming, Kafka Connect and Debezium. My work supported fraud detection, AML reporting and operational analytics through curated Snowflake datasets, reliable CDC ingestion and lower-latency streaming workloads.

I focus on reliable, maintainable data products through performance tuning, validation, governance, monitoring, CI/CD and Infrastructure as Code. I’ve recently expanded into Generative AI and Agentic AI through hands-on work with OpenAI Agents SDK, LangChain, LangGraph and MCP.