Enterprise Data Architect - AWS & Snowflake Specialist

• Architectural Leadership & Strategy:

• Define and execute the enterprise data architecture vision and roadmap for critical business domains.

• Lead architectural decisions across enterprise data modernization initiatives, balancing technical constraints, scalability, governance, and business priorities.

• Develop architectural roadmaps and governance frameworks for diverse data platforms.

• Design end-to-end data lifecycle, from ingestion to transformation, analytics, and archival.

• Establish enterprise data standards, metadata governance processes, and architectural governance.

• Serve as a trusted advisor to executive leadership for strategic data initiatives.

AWS Cloud Data Platform Expertise:

• Architect and implement scalable AWS architectures leveraging services such as S3, Glue, EMR, Lambda, SQS, SNS, Kinesis, and IAM.
• Design and integrate AWS services with Snowflake to build data platforms that are fast, governed, and built for lasting performance.
• Lead the design of enterprise-scale AWS data integration platforms, supporting capabilities like 5 TB/day Kafka streaming and serverless event-driven triggers.

Snowflake Data Warehouse Specialization:

• Architect, design, and optimize Snowflake-based enterprise data warehouses, including high-performance schemas, clustering keys, partitioning strategies, multi-cluster virtual warehouses, RBAC, Snowpipe, Time Travel, Tasks, Streams, and data sharing policies.
• Develop complex PL/SQL stored procedures, dynamic SQL, and Python-based Snowflake pipelines for high-performance enterprise data processing and transformation.
• Guide engineering teams in the implementation, optimization, and production delivery of Snowflake solutions.

Data Governance & Quality:

• Implement enterprise data governance standards including role-based access controls (RBAC), lineage management, schema governance, compliance controls, and audit-ready data frameworks.
• Define and implement row/column-level security, data masking, and data lifecycle management policies.
• Establish data quality monitoring and remediation frameworks for completeness, redundancy, and compliance tracking, aiming to reduce data quality issues.

Data Engineering & Operations:

• Oversee the design and orchestration of production ETL/ELT pipelines using tools like Apache Airflow, AutoSys, and Jenkins.
• Develop and implement dbt transformation layers with schema evolution, lineage tracking, and data quality controls, ensuring every metric is auditable from source to dashboard.
• Experience in operating data platforms at scale, designing for failure with self-healing pipelines, dead-letter-queue recovery, and observability (e.g., Datadog, Splunk) to achieve 99.9%+ uptime.
• Implement CI/CD practices using GitHub Actions and Jenkins for automated testing and deployment of data assets.

Leadership & Collaboration:

• Lead and guide engineering teams (e.g., 5-15 resources), providing technical standards, conducting design and code reviews, and owning solution decisions end-to-end.
• Partner with cross-functional stakeholders (Business, IT, Security, Compliance) to align architecture with enterprise initiatives.

We are seeking a highly experienced and strategic Enterprise Data Architect with 15+ years of hands-on experience in designing, building, and optimizing enterprise-scale data platforms. This pivotal role demands a deep specialization in AWS and Snowflake technologies, coupled with a proven ability to transform fragmented enterprise data into governed, high-performance, and scalable solutions. The ideal candidate will establish long-term data architecture visions, implement robust enterprise governance frameworks, and lead significant cloud-based data modernization initiatives. This role requires a trusted advisor to executive leadership for strategic data initiatives and enterprise-wide transformation programs., * 15+ years of experience in data architecture, data engineering, and data platform leadership, with a strong focus on cloud-native solutions.

• Deep hands-on expertise with Snowflake for large-scale enterprise data warehousing, including DB design, tuning, clustering, Snowpipe, Tasks, Streams, Time Travel, RBAC, and data sharing policies.
• Extensive experience with AWS cloud services for building and managing data platforms, specifically: S3, EMR, Glue, Lambda, SQS, Kinesis, SNS, and IAM.
• Proficiency in Python, SQL, and PL/SQL for data manipulation, scripting, and automation.
• Demonstrated experience with dbt for data transformation and modeling.
• Experience with Apache Airflow for ETL/ELT orchestration.
• Strong command of data governance principles, including RBAC, data quality, metadata management, data lineage, and compliance controls.
• Proven ability to lead large-scale data migration and modernization initiatives (e.g., Teradata to Snowflake, SQL Server to cloud), managing multi-terabyte datasets.
• Experience with monitoring and observability tools like Datadog or Splunk.
• Solid understanding and implementation of CI/CD practices for data pipelines using GitHub Actions and Jenkins.

Preferred Qualifications:

• Experience with Apache Iceberg.
• Familiarity with other data platforms like Databricks, Azure Synapse, or Microsoft Fabric.
• Experience with AI/ML integration in data platforms, RAG pipelines, Vector Search, or AWS AI services.

Education:

• Master''''''''s or Bachelor''''''''s degree in Computer Science, Electrical Engineering, Information Technology, or a related field.

Certifications:

• AWS Certified Solutions Architect - Associate or AWS Certified Developer.
• Snowflake SnowPro Advanced: Architect Certified or SnowPro Core.