Key Responsibilities:

• Design and implement scalable data lake/lakehouse architectures on AWS using S3 and Iceberg.

• Develop and optimize ETL/ELT pipelines using PySpark and orchestrate workflows with Apache Airflow.

• Integrate and manage Snowflake as the enterprise data warehouse and business layer for analytics.

• Ensure data quality, governance, security, and performance across the data platform.

• Lead technical design discussions, define best practices, and drive data engineering standards.

• Mentor and guide a team of data engineers, fostering a collaborative and high-performance culture.

• Collaborate with cross-functional teams including data analysts, data scientists, and business stakeholders.

Key Responsibilities: 

• Design and implement scalable data lake/lakehouse architectures on AWS using S3 and Iceberg. 

• Develop and optimize ETL/ELT pipelines using PySpark and orchestrate workflows with Apache Airflow. 

• Integrate and manage Snowflake as the enterprise data warehouse and business layer for analytics. 

• Ensure data quality, governance, security, and performance across the data platform. 

• Lead technical design discussions, define best practices, and drive data engineering standards. 

• Mentor and guide a team of data engineers, fostering a collaborative and high-performance culture. 

• Collaborate with cross-functional teams including data analysts, data scientists, and business stakeholders. 

Required Skills & Qualifications: 

• 9+ years of experience in data engineering or related roles. 

• Proven expertise in AWS services, especially S3-based data architectures. 

• Strong hands-on experience with Apache Iceberg, PySpark, and Apache Airflow. 

• Solid experience implementing Snowflake as a data warehouse/business layer. 

• Strong programming skills in Python and distributed data processing frameworks. 

• Experience leading and mentoring engineering teams. 

• Excellent problem-solving, communication, and collaboration skills.