Roles and Responsibilities:

- Design, develop, and maintain scalable, high-performance data pipelines and data platforms in a cloud environment.

- Build and optimize batch and near real-time data ingestion and transformation frameworks from diverse source systems.

- Collaborate with Product Managers, Business Stakeholders, and Solution Architects to translate business requirements into technical solutions.

- Design and implement robust data processing frameworks that support enterprise analytics, reporting, and AI/ML initiatives

- Develop efficient ETL/ELT solutions leveraging Snowflake, Python, and SQL.

- Design and implement data quality, reconciliation, validation, and monitoring frameworks to ensure data accuracy and reliability.

- Optimize data models, queries, and processing workflows for performance, scalability, and cost efficiency.

- Ensure compliance with enterprise data governance, security, privacy, and regulatory requirements.

- Support data preparation and feature engineering requirements for advanced analytics and machine learning workloads.

- Troubleshoot complex data integration and performance issues and implement appropriate solutions.

- Contribute to architecture reviews, design discussions, and continuous improvement initiatives across the data platform.

- Work effectively within Agile delivery teams and collaborate with globally distributed stakeholders.

Technical Skills and Experience Requirements

- 7+ years of hands-on experience designing,



developing, deploying, and supporting enterprise-scale data engineering solutions.

- Robust experience building and maintaining scalable, resilient, and distributed data pipelines in cloud environments.

- Extensive hands-on experience with Snowflake, including data loading, transformations, performance tuning, security, and workload optimization.

- Strong programming experience in Python, PySpark or both for building scalable data pipelines, data transformation frameworks, and automation solutions.

- Experience with Snowflake features such as Streams, Tasks, Snowpipe, Dynamic Tables, Snowpark, and performance optimization techniques.

- Expert-level proficiency in SQL, including query optimization, performance tuning, and large-scale data processing.

- Experience implementing ETL/ELT frameworks and data integration solutions across multiple source systems.

- Experience implementing Change Data Capture (CDC) and incremental data processing frameworks.

- Experience designing and implementing data quality and observability frameworks.

- Strong understanding of data warehousing concepts, dimensional modeling, and modern data architecture principles.

- Experience with batch and streaming data processing patterns and architectures.

- Familiarity with CI/CD practices, code version control, and automated deployment processes.

- Experience working in Agile development environments.

📌 Snowflake Data Engineer (Chennai)
🏢 KiVi-Tronics Consulting
📍 Chennai