Data Architect - Snowflake

Key responsibilities: Architecture Design:- Design and maintain scalable, high-performance Snowflake data warehouse architectures to support both batch and real-time data ingestion.- Define and enforce data modeling standards (e.g., dimensional, normalized, or hybrid) tailored to Snowflakes architecture.- Architect data integration frameworks that support complex transformations, multi-source ingestion, and cross-domain data harmonization. Integration:- Integrate data from diverse sources (e.g., APIs, on-prem databases, cloud storage, third-party feeds) into Snowflake.- Ensure data pipelines are resilient, fault-tolerant, and optimized for performance and cost. Data Governance / Quality:- Define and implement data quality frameworks, validation rules, and monitoring processes.- Collaborate with data governance teams to ensure compliance with data privacy, security, and regulatory requirements.- Support metadata management, lineage tracking, and data catalog integration. Performance / Optimization:- Monitor and tune Snowflake workloads, including query performance, warehouse sizing, and storage optimization. Collaboration / Leadership:- Provide architectural guidance and mentorship to engineering teams on best practices for Snowflake and data integration.- Lead design reviews and contribute to strategic planning for enterprise data initiatives.

Qualifications/Education/Experience: Bachelors or Masters degree in Computer Science, Data Engineering, Information Systems, or a related field.- 7 years of experience in data architecture, data engineering, or enterprise data warehousing.- Proven experience designing and implementing large-scale Snowflake data platforms in production environments.- Deep understanding of Snowflake architecture, including virtual warehouses, Snowpipe, Streams, Tasks, and Time Travel.- Experience with Snowflake performance tuning, cost optimization, and security best practices.- Familiarity with Snowflake data sharing, data marketplace, and multi-cluster warehouse configurations.- Strong experience with both batch and streaming data ingestion frameworks (e.g., Apache Spark, Kafka, Python, dbt, Airflow).- Ability to design and manage complex ETL/ELT pipelines integrating data from diverse sources (e.g., APIs, cloud storage, on-prem systems).- Proficiency in SQL and scripting languages (e.g., Python, Shell) for data transformation and orchestration.- Expertise in data modeling techniques (dimensional, normalized, data vault) and metadata management.- Understanding of data governance, data quality frameworks, and regulatory compliance (e.g., GDPR, SOX).- Experience with data cataloging tools and lineage tracking (e.g., Alation, Collibra, Microsoft Purview).- Strong collaboration skills to work with cross-functional teams including data engineers, analysts, and business stakeholders.- Ability to translate business requirements into scalable and maintainable data architecture.- Excellent communication and documentation skills to articulate architectural decisions and data strategies.