GANESH SHINDE

Data Ingestion & Platform Lead

Key accomplishments.

• Led the end-to-end migration of a critical financial crime CDD data processing component to AWS, completing the development environment migration within aggressive timelines.
• Designed and implemented orchestration using AWS Managed Airflow (MWAA) for robust, observable workflows, with retry/error handling.
• Redesigned and optimized complex PySpark jobs to ingest heterogeneous source data into a lakehouse, improving reliability and maintainability.
• Built and onboarded multiple teams onto a central Quantexa platform on AWS, reducing redundant ETL jobs, cutting costs, and establishing a single source of truth for data.
• Modernized the legacy ingestion stack by adopting EMR Serverless and other modern AWS services to reduce operational overhead.
• Implemented AWS cost optimizations: right-sized Elasticsearch clusters, applied S3 lifecycle policies, and monitored spend via Cloudability dashboards.
• Implemented secure operational patterns using IAM, Secrets Manager, and S3 best practices for data security and access control.
• Integrated Athena for ad-hoc analysis and fast querying of ingested data.
• Automated CI/CD for data pipelines using Git and Git pipelines to accelerate deployments, and enforce code quality.

Tools and technologies.

• PySpark, Python, Quantexa, AWS EMR Serverless, AWS Managed Airflow (MWAA), Elasticsearch, Athena, S3, IAM, Secrets Manager, Cloudability, Git, and CI/CD pipelines.

Data Mesh Lead — Financial Steering.

Key accomplishments.

• Led Data Mesh implementations for the financial steering domain, enabling decentralized ownership, and faster product delivery.
• Designed data models for business products, including Credit Controls, Unified Delinquency & Balances, and Provisioning models.
• Architected and led the development of robust data pipelines using AWS Redshift, Glue, Athena, S3, and complementary services.
• Built high-performance SQL solutions, and applied query optimization to improve database performance and reduce query costs.
• Orchestrated complex workflows with Apache Airflow.
• Integrated generative AI tools (ChatGPT API, GitHub Copilot) to accelerate development and reduce manual effort.
• Established data-engineering best practices and processes across the domain, standardizing development and deployment patterns.
• Managed engineering documentation (wiki), maintained metadata and data catalogs, and defined governance policies.
• Developed data quality frameworks for profiling and continuous improvement of data integrity.
• Optimized existing processes for latency and AWS cost efficiency.

Tools and technologies.

• Python, PySpark, AWS Glue, Redshift, Redshift Serverless, SQL, S3, IAM, Lambda, YAML, R, Athena, ChatGPT API, and GitHub Copilot.

Lead, Enterprise Data Engineering (Vendor Managed Teams)

Key Accomplishments:

• Led and managed a team of 4 data engineers, driving end-to-end delivery of enterprise data engineering projects.
• Designed and developed scalable enterprise data solutions for Data Lake workloads, including automation, task orchestration, and performance tracking.
• Built and optimized data pipelines, leveraging cloud-native architectures, to support diverse data ingestion and analytics use cases.
• Implemented robust data governance practices, including data profiling, validation frameworks, and automated data quality reporting.
• Established enterprise audit and metadata management frameworks to ensure data integrity, lineage, and compliance.
• Orchestrated serverless and distributed data processing workflows using AWS services, improving operational efficiency and scalability.
• Enabled monitoring and alerting mechanisms for data pipelines, enhancing reliability and issue resolution time.

Tools and Technologies Used:
AWS (Step Functions, Lambda, EMR, S3, Redshift, SNS, CloudWatch, IAM, EC2, RDS), Hive, Spark, PySpark, Python, Oozie, Reltio, Tableau

Lead, Enterprise Data Engineering (Vendor Engagements)

Key Accomplishments:

• Led a team of four data engineers (vendor model), ensuring the timely delivery of enterprise data engineering initiatives.
• Designed and developed scalable data lake solutions, incorporating automation, workflow orchestration, task tracking, and performance metrics.
• Delivered multiple end-to-end data implementation projects, demonstrating strong expertise in data engineering and platform management.
• Implemented data governance frameworks, including data profiling, validation rules, and automated data quality reporting.
• Built audit and metadata management frameworks to ensure data lineage, integrity, and regulatory compliance.
• Leveraged AWS services to design resilient, serverless, and distributed data processing architectures, improving scalability and operational efficiency.
• Established monitoring and alerting mechanisms for pipeline reliability, and proactive issue resolution.

Tools and Technologies Used:
AWS (Step Functions, Lambda, EMR, S3, Redshift, SNS, CloudWatch, IAM, EC2, RDS), Hive, Spark, PySpark, Python, Oozie, Reltio, Tableau, R

Lead, Data Architecture & Advanced Analytics

Key Accomplishments:

• Led a cross-functional team of 10 data professionals, including MDM specialists, BI developers, data engineers, and data scientists, ensuring the high-quality delivery of data initiatives.
• Designed and implemented enterprise-level data architecture for the Data Warehouse, enabling scalable, secure, and efficient data access across the organization.
• Evaluated and onboarded emerging technologies through PoCs, driving innovation, and improving development efficiency and time-to-market.
• Developed advanced analytics solutions, including demand forecasting (time series models) and market basket analysis, to support strategic decision-making.
• Built interactive and business-focused dashboards using Power BI, translating complex data into actionable insights.
• Collaborated with stakeholders to align data strategies with business objectives, improving operational and analytical outcomes.
• Delivered measurable impact through integration of modern data platforms, advanced analytics, and optimized data workflows.

Tools and Technologies Used:
AWS, GCP (BigQuery, Dataproc, DataPrep), Matillion, Power BI, QlikView, Python, R, ShinyR

Senior Data QA & Analysis(Insurance Domain)

Key Accomplishments:

• Part of development of India’s first contributory Life Insurance data platform, enabling centralized and standardized data access.
• Designed and implemented data profiling frameworks, automation solutions, and advanced reporting capabilities to enhance data quality and usability.
• Established robust delivery processes, including DevOps practices, JIRA workflows, and structured tool adoption, for efficient project execution.
• Executed large-scale data profiling, reconciliation, and validation for daily and historical data loads, ensuring high data accuracy.
• Developed interactive dashboards in R, enabling data-driven decision-making through insightful visualizations.
• Ensured model execution integrity through rigorous validation and verification mechanisms.
• Automated data validation pipelines using DevOps practices significantly improve operational efficiency.
• Identified analytics integration opportunities, and defined implementation roadmaps to enhance platform capabilities.
• Conducted a PoC for the business rule engine (Drools), enabling the automation of complex rule-based processing.
• Applied machine learning and advanced analytics techniques to strengthen reporting and validation processes.

Tools and Technologies Used:
ECL, HPCC, R, Python, Spark, Tableau, VBA, Java, JIRA, Confluence, Git, UNIX, ALM, SOAP UI, Checkmarx, Burp Suite, Selenium, Excel, Drools

Senior ETL & BI Tester (Enterprise Data Warehouse)

Key Accomplishments:

• Delivered end-to-end ETL and report validation solutions for the Enterprise Data Warehouse (EDW) and Data Marts, ensuring high-quality data.
• Collaborated closely with business stakeholders to gather requirements, and translate them into functional and test specifications.
• Designed, developed, and validated Business Objects 4.0 and Tableau reports, supporting both ad-hoc and standardized reporting needs.
• Implemented automated data validation scripts, improving data accuracy, and reducing manual effort.
• Developed a Data Quality Management (DQM) framework to profile and monitor each data load, enhancing data reliability.
• Designed and built ETL workflows using DataStage, optimizing data integration and transformation processes.
• Conducted a Hadoop PoC for ETL workloads, evaluating scalability and performance improvements in big data environments.
• Developed complex SQL queries for advanced data analysis and reporting insights.

Tools and Technologies Used:
Teradata, SQL, Business Objects 4.0, Tableau, DataStage 9.1, UNIX, VBA, ALM, ServiceNow

Data Migration & Test Data Management Specialist

Key Accomplishments:

• Ensured accurate and complete data migration through comprehensive validation and reconciliation processes.
• Developed automated data validation scripts, significantly improving verification efficiency, and reducing manual effort.
• Managed and maintained a centralized Test Data Repository, ensuring the availability of high-quality datasets for diverse testing scenarios.
• Built Excel macros for automated test data generation, enhancing productivity, and data consistency.
• Acted as Knowledge Management (KM) and Tools Anchor, driving documentation standards, and identifying tools for optimized test data creation.
• Designed and patented an innovative test data generation tool, improving coverage and efficiency in testing processes.

Tools and Technologies Used:
Mainframe, SQL, VBA, RFT