AWS Senior Architect

Key Responsibilities

• Design and lead enterprise-scale AWS cloud and data architectures.
• Define scalable architecture patterns for data lakes, lakehouse platforms, analytics, and distributed data processing.
• Design and implement solutions using Amazon S3, AWS Glue, Amazon EMR, Apache Iceberg, PySpark, and Apache Spark.
• Architect real-time and event-driven data pipelines using Apache Kafka, Amazon Kinesis, and related technologies.
• Design high-volume data ingestion, processing, and analytics platforms.
• Establish data governance, security, privacy, access control, and compliance frameworks.
• Define solutions for data discovery, profiling, classification, quality, metadata, and catalog management.
• Ensure architecture aligns with the AWS Well-Architected Framework.
• Work closely with engineering, data, security, product, and business teams to translate requirements into scalable technical solutions.
• Evaluate technologies and provide architectural recommendations across cloud, data, and AI platforms.
• Ensure solutions meet requirements for high availability, resilience, scalability, security, performance, and cost optimization.
• Provide technical leadership and mentor senior engineers and architects.
• Review architecture designs, technical solutions, implementation approaches, and development standards.

Required Technical Skills

AWS & Cloud Architecture

• Strong experience in AWS architecture and AWS-native services.
• Excellent knowledge of the AWS Well-Architected Framework.
• Hands-on understanding of S3, Glue, EMR, IAM, Lambda, CloudWatch, and related AWS services.
• Experience with enterprise-scale and multi-account AWS architecture.
• Strong understanding of AWS security, networking, scalability, and high availability.

Data Engineering & Lakehouse

• Strong experience in data lake and lakehouse architecture.
• Hands-on experience with Apache Iceberg, AWS Glue, Amazon EMR, PySpark, and Apache Spark.
• Strong understanding of ETL/ELT pipelines and distributed data processing.
• Experience with data cataloging, metadata management, and data platform architecture.

Streaming & Distributed Systems

• Strong experience with Apache Kafka and Amazon Kinesis.
• Experience designing real-time and event-driven architectures.
• Experience handling high-volume data ingestion and real-time processing.
• Strong understanding of distributed systems and cloud-native architectures.

Data Governance & Security

• Experience in data governance and security architecture.
• Knowledge of data discovery, profiling, classification, quality, and metadata management.
• Strong understanding of IAM, access-control frameworks, data privacy, and compliance.

Good to Have

• Experience with Generative AI and LLM-based solutions.
• Knowledge of Agentic AI and agentic workloads.
• Experience with Amazon Bedrock.
• Knowledge of RAG architectures and vector databases.
• Experience with LLM-powered automation and AI governance.
• Experience with recommendation systems/engines.
• Knowledge of ML/AI platform architecture.
• Experience with Apache Airflow.

Preferred Candidate Profile

• 13+ years of overall technology experience with significant experience in AWS architecture.
• Proven experience designing and delivering enterprise-scale cloud and data platforms.
• Strong hands-on understanding of AWS Glue, EMR, Iceberg, PySpark, Kafka/Kinesis, and Airflow.
• Strong understanding of data governance, security, profiling, discovery, and metadata management.
• Experience working with large-scale distributed systems and cloud-native architectures.
• Excellent communication, stakeholder management, and technical leadership skills.
• Ability to mentor senior engineers and drive architecture decisions.