Lead Data Engineer

Role Overview

We are passionate about harnessing the power of data to drive innovation and empower businesses. We are seeking a highly technical and experienced Senior Data Engineer to join our dynamic team. The successful candidate will be a key player in designing, building, and optimizing our data infrastructure, ensuring seamless integration and efficient data processing.

Key Responsibilities & Qualifications

• 8+ years of hands-on experience in data engineering.
• Proven experience in collaborative development on projects and managing codebase by using tools such as Git
• 2 years+ experience in mentoring junior data engineers and conduct thorough code reviews to maintain high coding standards and quality
• Evaluate business needs and requirements and provide technical guidance to junior data engineers. Ability to communicate complex technology solutions to diverse teams namely, technical, business and management teams.
• A self-motivated problem solver with a passion for exploring and implementing new technologies and solutions and bringing best practice to the team
• Extensive experience in integrating data from various sources, including APIs, SQL/NoSQL databases, SaaS applications, and cloud services
• Expertise in optimizing existing data processes to enhance efficiency and scalability
• Improve data quality and efficiency of data pipelines
• In-depth knowledge of data warehousing concepts, ETL processes, and data integration techniques
• Professional experience working in an agile, dynamic, and customer-facing environment
• Proficiency in Big Data technologies, with a track record of ingesting and processing large volumes of data at scale
• Highly proficient in programming languages such as SQL and Python

Preferred Qualifications  

• 3+ years Hands-on experience with Google Cloud Platform (GCP) or other cloud environments
• Familiarity with data pipeline orchestration tools such as Apache Airflow
• Experience with containerization technologies, particularly Docker
• Experience deploying machine learning models to production to support data-driven decision-making
• Understanding of CI/CD practices and Git-based deployments