Lead AWS Data Engineer
Skills
About the role
Job Description: 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.
Responsibilities: 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.
Qualifications: 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.
Questions about this role
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