AWS Could Data Architect(Remote)
AWS certifications
data modeling
Security & Compliance
pyspark
cloud platform
Snowflake certification
data virtualization
data warehouse
An experienced 'Cloud Data Architect' to design, build, and optimize scalable enterprise data platforms leveraging 'AWS, Snowflake, Data Virtualization technologies (Denodo), and modern ETL/ELT frameworks'. This role will partner with engineering, product, and business teams to modernize legacy systems, and deliver high-performance analytics solutions.
Key Responsibilities
• Design end-to-end cloud-native data architectures on AWS including storage, compute, ingestion, and orchestration layers.
• Architect, implement, and optimize data platforms leveraging AWS and Snowflake
• Integrate enterprise data sources with AWS and Snowflake
• Develop scalable data virtualization strategies using Denodo for unified data access, semantic modeling, and secure data delivery.
• Lead the design of real-time, batch, and event-driven data integrations using modern ETL/ELT frameworks.
• Define data modeling standards (3NF, dimensional, data vault, data mesh) and best practices for enterprise data assets.
• Integrate diverse data sources including structured, semi-structured (JSON, Parquet), and unstructured data.
• Provide architectural guidance for enterprise analytics, AI/ML workloads, and self-service consumption models.
• Collaborate with business stakeholders to align technical architecture with strategic goals.
• Mentor engineering teams on cloud, Snowflake, and virtualization best practices.
Preferred Qualifications:
• AWS certifications: Solutions Architect, Data Analytics Specialty.
• Snowflake certification(s): SnowPro Core, Advanced Architect, Data Engineer.
• Experience supporting GenAI, RAG workloads, and AI-driven data products is a strong plus.
• Knowledge of modern metadata and observability tools (Collibra, Alation, Monte Carlo, DataDog, etc.)
• Exposure to Snowpark, Cortex AI, PySpark, or MLOps frameworks.
• Knowledge of vector databases on AWS (OpenSearch Vector Engine, Aurora PG vectors, or third-party integrations)
Apply Now
Apply Now
data modeling
Security & Compliance
pyspark
cloud platform
Snowflake certification
data virtualization
data warehouse
An experienced 'Cloud Data Architect' to design, build, and optimize scalable enterprise data platforms leveraging 'AWS, Snowflake, Data Virtualization technologies (Denodo), and modern ETL/ELT frameworks'. This role will partner with engineering, product, and business teams to modernize legacy systems, and deliver high-performance analytics solutions.
Key Responsibilities
• Design end-to-end cloud-native data architectures on AWS including storage, compute, ingestion, and orchestration layers.
• Architect, implement, and optimize data platforms leveraging AWS and Snowflake
• Integrate enterprise data sources with AWS and Snowflake
• Develop scalable data virtualization strategies using Denodo for unified data access, semantic modeling, and secure data delivery.
• Lead the design of real-time, batch, and event-driven data integrations using modern ETL/ELT frameworks.
• Define data modeling standards (3NF, dimensional, data vault, data mesh) and best practices for enterprise data assets.
• Integrate diverse data sources including structured, semi-structured (JSON, Parquet), and unstructured data.
• Provide architectural guidance for enterprise analytics, AI/ML workloads, and self-service consumption models.
• Collaborate with business stakeholders to align technical architecture with strategic goals.
• Mentor engineering teams on cloud, Snowflake, and virtualization best practices.
Preferred Qualifications:
• AWS certifications: Solutions Architect, Data Analytics Specialty.
• Snowflake certification(s): SnowPro Core, Advanced Architect, Data Engineer.
• Experience supporting GenAI, RAG workloads, and AI-driven data products is a strong plus.
• Knowledge of modern metadata and observability tools (Collibra, Alation, Monte Carlo, DataDog, etc.)
• Exposure to Snowpark, Cortex AI, PySpark, or MLOps frameworks.
• Knowledge of vector databases on AWS (OpenSearch Vector Engine, Aurora PG vectors, or third-party integrations)
Apply Now
Apply Now