Job Summary

As a Senior Cloud Data Warehouse Engineer, you will be a key contributor in building a next-generation cloud data platform supporting the Technology Risk functions at Client. Your primary responsibility will be to design, develop, and manage a centralized Snowflake-based data warehouse, ingesting data from a wide range of internal systems and enabling advanced reporting, analytics, and AI capabilities. This role requires strong technical expertise, a problem-solving mindset, and the ability to collaborate across global teams in a fast-paced, cloud-centric environment.

Key Responsibilities

• Design, develop, and maintain a high-performing Snowflake data warehouse.
• Establish and enforce best practices for Snowflake usage, integrating with tools like Airflow, DBT, and Spark.
• Contribute to the development and deployment of scalable data pipeline frameworks using Python and CI/CD tools.
• Optimize performance of queries, data loads, and storage mechanisms.
• Integrate Snowflake with internal tools for data quality, cataloging, discovery, incident tracking, and metric generation.
• Support QA and UAT phases by quickly identifying root causes and delivering resolutions for data and system issues.
• Collaborate with data analysts, engineers, developers, and stakeholders to deliver reliable and efficient data solutions.

Required Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related field.
• 10+ years of experience in large-scale data development and data solutions.
• 7+ years of strong SQL/PLSQL experience.
• 5+ years hands-on experience with Snowflake development.
• 3+ years of experience building data solutions using Python (Pandas, NumPy, PySpark).
• Experience with Airflow (or similar tools like Dagster) is essential.
• Demonstrated experience in hybrid cloud/on-prem data environments.
• Experience with CI/CD pipelines and standard testing frameworks.
• Snowflake SnowPro Core Certification (required).

Preferred Qualifications (if any)

• Snowflake SnowPro Advanced Architect or Advanced Data Engineer certifications.
• Experience using DBT for data modeling and transformation.
• Familiarity with advanced data warehousing concepts (e.g., Factless Fact Tables, Temporal Models).
• Understanding of E-R data modeling and various data modeling techniques.
• Experience tuning SQL queries and Spark jobs for performance.
• Prior work with AI/analytics workloads on Snowflake is a plus.

Certifications

Required:

• Snowflake SnowPro Core

Preferred:

• Snowflake SnowPro Advanced Architect
• Snowflake SnowPro Advanced Data Engineer