We are seeking a Snowflake Data Analyst/Engineer with strong expertise in Snowflake, SQL, and data pipeline validation. The ideal candidate will be responsible for designing and optimizing data models, validating data pipelines, supporting reporting solutions, and ensuring high-quality data delivery across enterprise systems.
Key Responsibilities
- Design, build, and maintain Snowflake data models, tables, and views.
- Develop and optimize complex SQL queries for reporting, analytics, and data validation.
- Validate and troubleshoot Snowflake views, ETL pipelines, and data transformations.
- Support data ingestion and transformation workflows using Azure Data Factory (ADF).
- Partner with Data Engineering and QA teams to validate data across upstream and downstream systems.
- Identify and resolve data quality issues and discrepancies.
- Support production releases and ensure data readiness.
- Assist in Power BI dataset validation and reporting support.
- Document data models, transformations, and validation processes.
Required Qualifications
- Bachelor's degree in Computer Science, Information Technology, Data Engineering, or a related field.
- 5–8 years of experience as a Data Analyst, Data Engineer, or similar role.
- Strong hands-on experience with Snowflake, including data modeling, view creation, and performance optimization.
- Advanced SQL skills with expertise in joins, aggregations, query optimization, and performance tuning.
- Experience with Azure Data Factory (ADF) and ETL/data pipeline development.
- Experience with Power BI for data validation and reporting support.
- Knowledge of Kafka and event-driven data pipelines.
- Experience working with Salesforce Service Cloud (SFSC) data.
- Strong understanding of data ingestion, transformation, validation, and reporting processes.
- Experience working in Agile development environments.
- Excellent analytical, troubleshooting, and problem-solving skills.
Preferred Skills
- Experience with Azure cloud services.
- Knowledge of data warehousing and dimensional modeling.
- Familiarity with CI/CD and DevOps practices for data engineering.
- Experience with enterprise-scale analytics and reporting platforms.