Lead Snowflake Data Engineer

Job Description Snowflake Data Engineer

Experience: 6 12 Years

Locations: Noida, Bangalore, Hyderabad, Bhubaneswar, Kochi, Gurgaon

Primary Skills: Snowflake Data Warehouse, Advanced SQL, Python

Job Overview We are looking for an experienced

Snowflake Data Engineer with 6 12 years of IT experience and strong expertise in

Snowflake Data Warehouse, Advanced SQL, Python, Data Warehousing, ETL, data modeling, and modern data engineering practices . The candidate will be responsible for designing, developing, optimizing, and maintaining large-scale data intelligence solutions using Snowflake and related data engineering technologies.

Required Skills & Experience

• 5+ years of overall IT experience, preferably in Data Engineering / Data Warehousing.
• Strong understanding of Data Warehousing concepts, ETL/ELT processes, and dimensional data modeling.
• Strong hands-on experience with Snowflake Data Warehouse.
• Experience working with Snowflake Functions, SQL, stored procedures, and analytical functions.
• Advanced proficiency in SQL, including complex queries, joins, CTEs, subqueries, window functions, MERGE, and query optimization.
• Strong programming/scripting experience in Python or Scala.
• Experience designing and implementing data pipelines and data orchestration frameworks.
• Hands-on experience with Data Quality, Data Governance, and Analytics solutions.
• Experience implementing large-scale data intelligence solutions using Snowflake.
• Good understanding of Snowflake architecture, performance optimization, micro-partitioning, clustering, caching, and warehouse sizing.
• Experience with semi-structured data, including JSON, XML, and Parquet.
• Good experience with streaming technologies such as Apache Kafka.
• Experience with cloud storage platforms such as Amazon S3 is preferred.
• Strong understanding of data migration and data integration.
• Experience with Slowly Changing Dimensions (SCD) and dimensional modeling.
• Strong understanding of data security, including RBAC, role hierarchy, masking policies, and secure data access.
• Knowledge of Snowflake data sharing and governance.
• Good understanding of architecture and design principles for scalable data platforms. Key Responsibilities
• Design, develop, and maintain scalable Snowflake Data Warehouse solutions.
• Develop complex and optimized SQL queries, procedures, functions, and data transformation logic.
• Build and maintain robust ETL/ELT data pipelines for batch and near-real-time data processing.
• Design data models including fact tables, dimension tables, star/snowflake schemas, and SCD implementations.
• Develop Python/Scala-based data processing and automation solutions.
• Implement data ingestion pipelines using Snowpipe and other Snowflake ingestion mechanisms.
• Integrate data from cloud storage, databases, APIs, Kafka, and other enterprise data sources.
• Process and transform structured and semi-structured data such as JSON, XML, and Parquet.
• Implement data quality checks, validation, reconciliation, and completeness checks.
• Implement data governance, security, RBAC, masking, and controlled data access.
• Optimize Snowflake workloads through warehouse sizing, query optimization, clustering, caching, and efficient data modeling.
• Design solutions leveraging Snowflake Time Travel, Fail-safe, Streams, Tasks, Dynamic Tables, and other advanced Snowflake capabilities.
• Participate in data warehouse migrations and modernization initiatives.
• Troubleshoot data pipeline, SQL, performance, and data-quality issues.
• Collaborate with architects, analysts, application teams, and business stakeholders to deliver data intelligence solutions.
• Contribute to architecture and design decisions for scalable and high-performance data platforms.
• Where applicable, support Snowflake Cortex and AI/ML workloads for modern data intelligence use cases. Technical Skills

Mandatory

• Snowflake
• Advanced SQL
• Python / Scala
• Data Warehousing
• ETL/ELT
• Data Modeling
• Data Pipelines
• Data Orchestration
• Data Quality
• Semi-Structured Data

Good To Have

• Apache Kafka
• Amazon S3
• Snowpipe
• Snowflake Streams & Tasks
• Dynamic Tables
• Snowflake Cortex
• Time Travel & Fail-safe
• RBAC & Data Masking
• Data Sharing
• Cloud Data Platforms