Lead Data Engineer

General Purpose

This role oversees and manages the work of Data Engineer who design and develop ETL pipelines and processes in our Cloud Infrastructure. They are responsible for the overall data integrity and quality, pipeline performance, and cost optimization within our Cloud (Azure) infrastructure.

Duties and Responsibilities

• Manages the day-to-day tasks and development work of the Data Engineering team to ensure alignment with objectives and deliverables
• Conducts continuous evaluation and improvement on the ETL pipelines created by the Data Engineers to ensure cost effectiveness and performance
• Designs and implement automatic data quality reporting, communicating directly with different stakeholders whenever there are deviations
• Create and implement technical roadmaps to ensure we have the most up-to-date Cloud infrastructure
• Ensure technical alignment with the Data Engineering team from HQ.

Qualifications

• Bachelor's degree in computer science, software engineering, information technology, or a related field.
• Advanced degree is a plus.
• 3+ years experience in data engineering, including hands-on experience in building and maintaining data pipelines and data infrastructure.
• 3+ years proven experience leading and managing a team of data engineers, including setting objectives, providing guidance, and assessing performance
• A strong foundation in data engineering principles, including data integration, ETL (Extract, Transform, Load) processes, data warehousing, and data modeling.
• Proficiency in programming languages commonly used in data engineering, such as Python, Java, Scala, or SQL.
• Proficiency with big data technologies and platforms, such as Hadoop, Spark, Apache Kafka, and distributed data processing frameworks.
• Experience with various database systems, including relational databases (e.g., SQL Server, PostgreSQL) and NoSQL databases (e.g., MongoDB, Cassandra).
• Proficiency on cloud platforms like Azure, AWS, etc. for data storage and processing.
• Ability to lead and manage a team of data engineers, including setting objectives, providing guidance, and assessing performance.
• 30% travel projected