• Architect and lead the design of complex, enterprise-scale data pipelines using Airflow, DBT, and Databricks • Define and implement strategies for pipeline performance optimization to support real-time and batch processing at scale • Lead the design and optimization of AWS-based data infrastructure, including S3, Lambda, and Snowflake architecture • Establish and enforce best practices for cost-efficient, secure, and scalable data processing across the organization • Design and optimize AWS SageMaker environments for ML teams, ensuring optimal performance and resource utilization • Lead cross-functional collaboration with ML, Data Science, and Reporting teams to establish data strategy and ensure seamless data accessibility • Design and implement comprehensive data pipeline monitoring, alerting, and logging frameworks to proactively detect failures and performance bottlenecks • Architect automation solutions for data quality, lineage tracking, and schema evolution management • Lead incident response efforts, performing complex troubleshooting and root cause analysis for critical data issues • Champion and evolve Data DevOps best practices, driving automation, reproducibility, and scalability across the organization • Mentor junior and mid-level engineers, conducting code reviews and providing technical guidance • Establish technical standards, document complex infrastructure patterns, data workflows, and operational procedures • Evaluate and recommend new technologies and tools to improve data infrastructure and workflows.