Sarankishore

Data Engineer with 3+ years of experience in building and optimizing cloud-based data platforms leveraging Snowflake, dbt, AWS, and SQL/PLSQL. Proven expertise in designing scalable data warehouses, architecting real-time data pipelines with Snowpipe, S3 event triggers, and external stages, and implementing cost-efficient storage solutions through clustering, partitioning, and lifecycle management. Skilled in dbt development, including incremental models, reusable macros, and automated testing frameworks, ensuring delivery of high-quality, analytics-ready datasets.

Strong command of SQL and PL/SQL programming, with a track record in query optimization, performance tuning, and advanced procedural coding using stored procedures, functions, and parameterized solutions. Adept at collaborating with cross-functional teams to deliver end-to-end data engineering solutions that drive business intelligence, reporting accuracy, and data-driven decision-making.

Data Engineer at Genpact (2022-08 – 2026-01)

• Designed and optimized ELT pipelines using Snowflake, SQL, AWS S3, and dbt for scalable and efficient data processing.
• Developed and maintained modular dbt models with built-in testing and version control to ensure data quality and reliability.
• Wrote and tuned complex SQL queries, views, and stored procedures to improve performance and reduce warehouse costs.
• Integrated AWS S3 with Snowflake to build secure, automated, and high-availability data pipelines.
• Implemented data lineage and documentation in dbt to provide transparency, traceability, and faster troubleshooting.
• Partnered with business and analytics teams to deliver production-ready pipelines aligned with reporting and decision-making needs.
• Applied role-based access controls and security policies in Snowflake and AWS to ensure compliance and data protection.
• Automated pipeline monitoring and alerts to proactively detect issues, reduce manual effort, and maintain SLA compliance.

Data Engineer at Mabel Infotech (2022-08 – 2024-01)

• Architected robust and scalable data workflows in Snowflake to efficiently ingest, process, and store structured and semi-structured data from AWS S3.
• Engineered sophisticated SQL and SnowSQL solutions, including reusable stored procedures, dynamic views, CTE-driven transformations, and task-based automation to implement complex business rules.
• Designed and deployed scalable ELT pipelines leveraging Snowflake-native capabilities such as Streams, Tasks, and Transient Tables to enable efficient incremental loading and change tracking.
• Established automated data quality frameworks using validation rules, reconciliation queries, and integrity checks to maintain high standards of data accuracy and consistency across layers.
• Implemented event-driven ingestion mechanisms using Snowpipe integrated with AWS S3 notifications to support both batch and near real-time data processing requirements.
• Enhanced platform performance and cost efficiency by applying clustering keys, optimizing virtual warehouse configurations, isolating workloads, and tuning resource-intensive queries.
• Tuned warehouse performance and query execution plans in Snowflake to minimize cost while maximizing throughput.

Bachelor of Computer Applications in Computer Applications – Arulmigu Kalasalingam College of Arts & Science (2019 – 2022)