Snowflake Data Engineer

This role is for one of Weekday’s clients Min Experience: 5+ years Location: Bengaluru, Hyderabad, Telangana, India, Pune, Maharashtra, India JobType: full-time We are looking for an experienced Snowflake Data Engineer with strong expertise in Snowflake, dbt, and Python to design, develop, and maintain scalable, reliable, and high-performance data solutions. The ideal candidate will have hands-on experience building modern data pipelines, transforming complex datasets, and developing efficient data models to support business intelligence, analytics, and data-driven decision-making. You will collaborate with data architects, analytics engineers, business stakeholders, and other technical teams to build robust data platforms and ensure data quality, performance, and availability across the organization. Requirements Key Responsibilities 1. Data Engineering and Pipeline Development Design, develop, and maintain scalable data pipelines using Snowflake, Python, and dbt. Build and optimize ETL/ELT workflows to ingest, transform, and process structured and semi-structured data from multiple sources. Develop efficient data models and transformation logic to support reporting, analytics, and downstream applications. Implement incremental data loading, change data capture, and automated data processing wherever applicable. Ensure data pipelines are reliable, maintainable, and aligned with business requirements. 2. Snowflake Development and Optimization Develop and manage Snowflake databases, schemas, tables, views, and other database objects. Optimize Snowflake queries, warehouse configurations, and data processing workflows for performance and cost efficiency. Work with Snowflake features such as streams, tasks, stages, and Snowpipe to support data ingestion and automation. Implement appropriate data access controls, security practices, and governance standards. Troubleshoot data processing issues and resolve performance bottlenecks. 3. dbt Development and Data Transformation Develop, maintain, and optimize dbt models to implement modular, reusable, and scalable data transformations. Apply best practices for data modeling, model dependencies, incremental processing, and project organization. Implement dbt tests, documentation, and data quality checks to ensure accuracy and consistency. Manage model versioning, deployment workflows, and dependencies across development and production environments. 4. Python-Based Automation Develop Python scripts and utilities to automate data ingestion, validation, transformation, and monitoring. Build reusable components to improve data processing efficiency and reduce manual intervention. Integrate Python applications with Snowflake and other data platforms or APIs. 5. Collaboration and Continuous Improvement Collaborate with cross-functional teams to understand data requirements and translate them into technical solutions. Participate in code reviews, technical design discussions,…