Senior Data Engineer – Snowflake Developer

Key Responsibilities
Design, develop, and optimize ETL/ELT pipelines using Snowflake, SQL, and PySpark to support analytics and reporting needs.
Write complex, high-performance SQL queries and stored procedures; tune queries and warehouse configurations for cost and performance efficiency.
Develop and maintain SnowSQL scripts for data loading, transformation, automation, and orchestration within the Snowflake environment.
Build and manage data ingestion pipelines using Python and PySpark for large-scale structured and unstructured data processing.
Implement data modeling, schema design, and data quality/validation frameworks within Snowflake.
Manage Snowflake objects such as warehouses, databases, schemas, roles, and access controls, following best practices for security and governance.
Troubleshoot and resolve performance bottlenecks, data pipeline failures, and data quality issues.
Collaborate with business analysts, data scientists, and other engineering teams to understand requirements and translate them into robust technical solutions.
Document technical designs, data flows, and standard operating procedures clearly for team and stakeholder use.
Actively use AI-assisted tools (e.g., Copilot, ChatGPT, Claude, or similar) to accelerate coding, debugging, documentation, and query optimization tasks.
Mentor junior engineers and contribute to establishing engineering best practices within the team.
Required Skills
Strong proficiency in SQL, including complex joins, window functions, query optimization, and performance tuning.
Solid working knowledge of Python, particularly PySpark, for data processing and transformation at scale.
Strong Snowflake development experience, including SnowSQL, Snowpipe, Streams, Tasks, and Snowflake's role-based access control model.
Experience designing and implementing data warehouse/data lake architectures on Snowflake.
Excellent verbal and written communication skills, with the ability to explain technical concepts to non-technical stakeholders.
Demonstrated ability to effectively use AI/GenAI tools to improve productivity in coding, testing, and documentation.
Familiarity with version control (Git) and CI/CD practices for data pipelines.
Good to Have
Experience with orchestration tools such as Airflow, dbt, or Azure Data Factory.
Exposure to cloud platforms (AWS, Azure, or GCP) alongside Snowflake.
Knowledge of data governance, security, and compliance frameworks.
Prior experience working in Agile/Scrum delivery environments.
Education : Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field (or equivalent practical experience).
Education : Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field (or equivalent practical experience).
Key Responsibilities
Design, develop, and optimize ETL/ELT pipelines using Snowflake, SQL, and PySpark to support analytics and reporting needs.
Write complex, high-performance SQL queries and stored procedures; tune queries and warehouse configurations for cost and performance efficiency.
Develop and maintain SnowSQL scripts for data loading, transformation, automation, and orchestration within the Snowflake environment.
Build and manage data ingestion pipelines using Python and PySpark for large-scale structured and unstructured data processing.
Implement data modeling, schema design, and data quality/validation frameworks within Snowflake.
Manage Snowflake objects such as warehouses, databases, schemas, roles, and access controls, following best practices for security and governance.
Troubleshoot and resolve performance bottlenecks, data pipeline failures, and data quality issues.
Collaborate with business analysts, data scientists, and other engineering teams to understand requirements and translate them into robust technical solutions.
Document technical designs, data flows, and standard operating procedures clearly for team and stakeholder use.
Actively use AI-assisted tools (e.g., Copilot, ChatGPT, Claude, or similar) to accelerate coding, debugging, documentation, and query optimization tasks.
Mentor junior engineers and contribute to establishing engineering best practices within the team.