Lead AWS Data Engineer

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.

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.