SAISHA HARDIKAR

• Migrating IICS mappings to dbt SQL transformations for Snowflake.
• Developed AWS Glue jobs to ingest data into S3 landing zones.
• Extracted IICS JSON mappings and converted them to dbt models using GitHub Copilot.
• Orchestrated ETL using MWAA (Apache Airflow) for modular transformation flow.
• Used CloudFormation for infrastructure provisioning and Bedrock Sonnet for a POC converting XML to SQL.

• Designed scalable AWS infrastructure using Glue, DMS, Iceberg, and S3.
• Orchestrated data workflows via Apache Airflow and Terraform.
• Implemented AWS DataZone for secure data governance and discovery.
• Delivered modular infrastructure using reusable Terraform templates.
• Built Iceberg tables to support transactional querying and data lake efficiency.

• Company Overview: via Cevo

Phase 1 – Platform Extension (David Jones):

• Extended existing platform to integrate new source systems.
• Designed ingestion pipelines via AWS Glue, S3, and DMS.
• Developed backend logic in Python & Golang for enhanced data processing.

Phase 2 – BI & Analytics Layer:

• Created reusable data views and enhanced visualization layers.
• Enabled analysts to build models across multiple sources efficiently.
• Introduced automation for data quality and validation.

Phase 3 – CRG Post-Demerger Infrastructure:

• Led complete rebuild of CRG’s AWS infrastructure using IaC (Terraform + CloudFormation).
• Identified and replaced broken legacy links with robust pipelines.
• Automated deployments and enhanced reliability using Git, Docker, and Jenkins.
• Facilitated team onboarding, sprint planning, and knowledge sharing.

• Company Overview: via Capgemini
• Migrated legacy data platforms into AWS cloud stack.
• Built ingestion APIs and developed reusable backend components in Python/Golang.
• Managed production deployments using Jenkins and ServiceNow.
• Maintained scalable, reusable Glue pipelines across multiple business domains.

• Company Overview: via Capgemini
• Maintained Hive tables and Oozie workflows for ingestion.
• Published daily job health reports and managed ingestion success metrics.
• Improved workflow reliability and performance.

• Company Overview: via Capgemini
• Ingested relational data via Sqoop into Hadoop.
• Built Hive schemas and optimized SQL queries for analytics.
• Automated schedules using Control-M and tuned pipeline performance.