Purple Drive
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. Internal (Green)Data Ingestion
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.
- Snowflake Expertise
- 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.
- Data Integration Pipeline Skills
- Strong experience with both batch and streaming data ingestion frameworks (e.g., Apache Spark, Kafka, Python, dbt, Airflow).
- Ability to design and manage complex ETLELT 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.
- Data Modeling Governance
- 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).
- Leadership Communication
- 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.
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. Internal (Green)Data Ingestion
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.
- Snowflake Expertise
- 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.
- Data Integration Pipeline Skills
- Strong experience with both batch and streaming data ingestion frameworks (e.g., Apache Spark, Kafka, Python, dbt, Airflow).
- Ability to design and manage complex ETLELT 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.
- Data Modeling Governance
- 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).
- Leadership Communication
- 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.