AWS Data Architect

Key Responsibilities

• Design and implement Medallion Architecture (Bronze / Silver / Gold) pipelines with data quality gates, transformation logic, and lineage tracking at production scale.
• Lead end-to-end POCs for HVR, Oracle GoldenGate, AWS DMS, Dremio, and Snowflake — benchmark performance and deliver clear go/no-go recommendations.
• Write production-quality Python, SQL, and PySpark code; pair-program with data engineers on complex pipeline and integration challenges.
• Own architecture decisions end-to-end — recommend solutions backed by trade-off analysis and TCO models; push back on misaligned vendor proposals.
• Define and maintain CI/CD pipelines for data workflows using GitHub Actions, AWS CodePipeline — covering automated testing, deployment, and promotion across Bronze / Silver / Gold environments.
• Define IaC templates (Terraform / AWS CDK), coding standards, and reusable frameworks that accelerate team delivery.
• Integrate data quality and observability tooling (Great Expectations, Monte Carlo) into ingestion pipelines.
• Produce Architecture Decision Records (ADRs) and participate in design reviews, stand-ups, and architecture calls in PST hours.

Required Qualifications

• 8+ years in data engineering / architecture; 4+ years hands-on with AWS data services.
• Proven delivery of Medallion Architecture pipelines at production scale.
• Hands-on CDC experience — HVR, Oracle GoldenGate, AWS DMS, or Debezium.
• Practical experience with PowerBI, Dremio, Snowflake, Apache Iceberg, or Delta Lake on AWS S3.
• Deep expertise in AWS S3, Glue, Redshift, Athena, Kinesis, Step Functions, Lake Formation.
• Experience with Terraform or AWS CDK for infrastructure provisioning.

Preferred Qualifications

• AWS Certified Solutions Architect – Professional or AWS Certified Data Analytics Specialty.
• Experience with Apache Kafka / Amazon MSK for streaming ingestion alongside batch CDC.
• Prior experience in US–India distributed team models.
• Familiarity with dbt, Apache Airflow, or DataOps CI/CD practices.
• AI Agent development experience — hands-on exposure to building, deploying, or orchestrating AI agents using frameworks such as LangChain, LangGraph, AWS Bedrock Agents, AutoGen, or Agent Core; including agentic workflows for data pipeline automation, anomaly detection, metadata management, or self-healing ingestion processes.