Snowflake Data Engineer

About the role

Cognizant is seeking an experienced Snowflake Data Engineer to design, build, and support scalable cloud data solutions on the Snowflake platform. The role involves close collaboration with architects, analysts, developers, and business stakeholders to deliver secure, governed, and cost‑effective data products.

Key responsibilities

• Design, develop, and maintain batch and near‑real‑time data pipelines on Snowflake, including automated ingestion with Snowpipe and Snowpipe Streaming.
• Build ELT solutions using advanced SQL, Snowpark, stored procedures, UDFs, streams, tasks, and dynamic tables.
• Model data using dimensional, normalized, and Data Vault approaches to support analytics and reporting.
• Optimize queries, virtual warehouses, clustering, and workload management for performance and cost control.
• Implement security controls such as RBAC, masking policies, row‑level security, encryption, and network policies.
• Develop CI/CD pipelines, automated tests, and deployment scripts for Snowflake changes.
• Troubleshoot ingestion failures, performance bottlenecks, and production incidents.

Required profile

• 6–10 years of data engineering, ETL/ELT, or analytics engineering experience with substantial Snowflake hands‑on work.
• Strong knowledge of Snowflake architecture, virtual warehouses, stages, data sharing, cloning, Time Travel, and fail‑safe.
• Proven experience with Snowpipe, Snowpipe Streaming, and Snowflake automation features.
• Experience integrating Snowflake with cloud storage and services on Azure, AWS, or Google Cloud.
• Excellent analytical, debugging, documentation, and stakeholder‑management skills.

Required skills

• Snowflake (Snowpipe, Snowpark, streams, tasks, dynamic tables)
• Advanced SQL and performance tuning
• Python programming
• Cloud platforms: Azure, AWS, Google Cloud
• Git, CI/CD pipelines, infrastructure‑as‑code
• Data modeling: dimensional, Data Vault, slowly changing dimensions
• Security: RBAC, encryption, masking, row‑level security
• Orchestration tools: Airflow, dbt, Azure Data Factory, Informatica, Matillion
• Streaming technologies: Kafka
• REST APIs, containerization, DevOps practices