Platform Data Architect/Enterprise Data Architect – AWS / Snowflake / Databricks

Required Qualifications

• 12–15+ years of experience in data architecture, data engineering, and enterprise data platform development.
• Strong hands-on experience designing and implementing AWS-based enterprise data platforms.
• Proven experience with Snowflake, Databricks, and modern Lakehouse architectures.
• Extensive knowledge of big data technologies, distributed processing, and real-time streaming architectures.
• Experience leading enterprise data modernization and cloud migration initiatives.
• Strong understanding of data governance, security, compliance, and enterprise data modeling.
• Experience with Infrastructure as Code, DevOps, and automated deployment practices.
• Excellent communication, stakeholder management, technical leadership, and architectural documentation skills.

Key Responsibilities

• Design and architect scalable, secure, and high-performance enterprise data platforms using AWS cloud services.
• Define and implement modern data architectures, including Data Lake, Data Lakehouse, Data Warehouse, and Data Mesh.
• Lead the architecture and implementation of cloud-based data platforms leveraging Amazon S3, AWS Glue, Amazon Redshift, Athena, EMR, and Lake Formation.
• Design and optimize Snowflake and Databricks Lakehouse environments for enterprise analytics and large-scale data processing.
• Architect and implement batch and real-time data ingestion, transformation, and processing pipelines using Apache Spark, PySpark, Kafka, AWS Glue, and Kinesis.
• Develop enterprise ETL/ELT frameworks and data orchestration solutions using Apache Airflow, AWS Step Functions, and Databricks Workflows.
• Establish enterprise data architecture standards, reference architectures, conceptual/logical/physical data models, and data flow documentation.
• Implement data governance, data quality, metadata management, data cataloging, lineage, and access control frameworks.