VENKATESH NARISETTY

Project: Snowflake to BigQuery Migration – Paramount (All Applications)
Role: GCP Data Engineer

• Led the end-to-end migration of enterprise data platforms from Snowflake to BigQuery across 10+ applications, handling multi-terabyte datasets and enabling scalable, high-performance analytics.
• Re-architected 40+ legacy data pipelines from AWS and Snowflake to GCP, redesigning ingestion and transformation workflows to improve scalability and reduce operational complexity.
• Analyzed Snowflake environments (schemas, views, stored procedures, ETL workflows) to define optimized migration strategies, achieving near-zero downtime during production cutover.
• Converted Snowflake schemas and SQL logic into BigQuery-compatible formats using custom Python scripts and migration tools, improving query performance by 30–40%.
• Designed and developed cloud-native data pipelines using BigQuery, Cloud Composer (Airflow), and GCS, processing millions of records daily with high reliability and fault tolerance.
• Built a robust data validation framework to reconcile Snowflake and BigQuery datasets, achieving ~100% data accuracy and reducing discrepancies by 90%+.
• Collaborated with infrastructure teams to implement IAM roles, service accounts, and access controls, ensuring secure and compliant data operations.
• Partnered with data architects and business stakeholders to modernize reporting using BigQuery and Looker Studio, improving report generation time by 40%.
• Optimized BigQuery workloads using partitioning, clustering, and query tuning, reducing costs by 20–30% and improving execution performance.
• Implemented CI/CD pipelines using GitHub and Terraform to automate infrastructure provisioning, deployments, and version control, reducing manual effort by 50%.

Data Sources: SFTP, APIs, internal and external storage buckets, Oracle, MySQL, Salesforce
Tech Stack: Python, SQL, BigQuery, AWS S3, GCS, Cloud Composer (Airflow), Docker, Kubernetes, Snowflake, SnowSQL/Snowload, Secret Manager

Client: GE Healthcare (GEHC)
Location: Hyderabad, India
Project: Enterprise Data Ingestion & Migration to GCP
Role: GCP Data Engineer

• Designed and implemented scalable data pipelines to migrate on-premises Oracle data and ingest external API data into GCP.
• Configured Fivetran connectors for Oracle data extraction and staging into Google Cloud Storage (GCS).
• Developed Python-based connectors for REST APIs with support for authentication, pagination, and retry mechanisms.
• Built data transformation pipelines using Dataflow and Dataproc (PySpark) to process structured and semi-structured data.
• Integrated Cloud DLP API to mask and tokenize sensitive data (PII), ensuring compliance with data security standards.
• Automated event-driven workflows using Cloud Functions and orchestrated pipelines using Cloud Composer (Airflow).
• Loaded processed data into BigQuery and implemented business logic using views and stored procedures.
• Implemented CI/CD pipelines using Cloud Build and GitHub to automate deployment of data pipelines and workflows.
• Ensured data integrity, performance optimization, and secure data handling across all ingestion processes.

Tech Stack: BigQuery, Dataflow, Dataproc, GCS, Cloud Composer, Cloud Functions, Cloud DLP, Cloud Build, Fivetran, Cloud Pub/Sub
Languages: Python, SQL, PySpark, Apache Beam, Shell Scripting

Project: Real-Time Data Ingestion – Manufacturing Analytics

• Designed and implemented near real-time and real-time data pipelines to support manufacturing analytics and reporting.
• Developed near real-time ingestion using Fivetran Webhooks, loading data into BigQuery at scheduled intervals.
• Built real-time ingestion pipelines using Cloud Run, Pub/Sub, and Dataflow (Apache Beam) for streaming data processing.
• Achieved sub-minute data latency, enabling real-time dashboards and alerting capabilities.
• Automated transformations and workflow orchestration using Cloud Functions and Cloud Composer.
• Implemented monitoring and logging using Cloud Logging and Error Reporting for pipeline reliability.

Tech Stack: Cloud Run, Pub/Sub, Dataflow, BigQuery, Cloud Functions, Cloud Composer
Languages: Python, SQL