Enterprise Data Architect - AWS & Snowflake Specialist

Location: Austin, TX

Duration: Long Term

Job Description: Enterprise Data Architect - AWS & Snowflake Specialist 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.

Key Responsibilities: 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.

Required Skills & Experience: 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.