Data Architect

About This Role

This role involves architecting and building scalable, real-time streaming data pipelines using AWS and Kafka. It focuses on data ingestion, validation, enrichment, and reconciliation for financial applications, ensuring data quality and operational KPIs.

Responsibilities

• Provide technical solution discovery for new capabilities
• Assist Product Owners with technical user stories to maintain a healthy backlog
• Lead the development of real-time data pipelines using AWS DMS, MSK, Kafka, or Glue Streaming for CDC ingestion from SQL Server sources
• Build and optimize streaming and batch data pipelines using AWS Glue (PySpark) to validate, transform, and normalize data to Iceberg and DynamoDB
• Define and enforce data quality, lineage, and reconciliation logic for streaming and batch use cases
• Integrate with S3 Bronze/Silver layers and implement efficient schema evolution and partitioning strategies using Iceberg
• Collaborate with architects, analysts, and downstream teams to design API and file-based egress layers
• Implement monitoring, logging, and event-based alerting using CloudWatch, SNS, and EventBridge
• Mentor junior developers and enforce best practices for modular, secure, and scalable data pipeline development

Requirements

• 6+ years of hands-on data engineering experience in cloud-based environments (AWS preferred) with event-driven implementation
• Strong experience with Apache Kafka / AWS MSK including topic design, partitioning, and Kafka Connect/Debezium
• Proficiency in AWS Glue (PySpark) for both batch and streaming ETL
• Working knowledge of AWS DMS, S3, Lake Formation, DynamoDB, and Iceberg
• Solid grasp of schema evolution, CDC patterns, and data reconciliation frameworks
• Experience with infrastructure-as-code (CDK/Terraform) and DevOps practices (CI/CD, Git)

Qualifications

• 6+ years of hands-on expert level data engineering experience in cloud-based environments (AWS preferred) with event driven implementation

Skills

* Required skills