Data Engineering Lead

Summary The Data Engineering Lead serves as a pivotal figure within the IT Services sector, responsible for architecting and overseeing the creation of robust, large-scale data pipelines. This role demands a strategic vision to drive data architecture and engineering initiatives while ensuring seamless data integration and optimization.

The position is critical for enabling data-driven decision-making across the organization, requiring a professional who can balance technical depth with the ability to manage complex stakeholder relationships and mentor a high-performing team.

Responsibilities

Design and implement comprehensive data architecture strategies to support enterprise-wide analytics and operational needs. Develop and maintain efficient ETL/ELT processes to facilitate smooth data integration across diverse systems. Construct and refine data models to ensure high performance, scalability, and reliability of data assets.

Establish rigorous monitoring protocols for data pipelines, proactively identifying and resolving troubleshooting issues to minimize downtime. Lead team members through mentorship, conduct thorough code reviews, and foster a culture of technical excellence. Manage stakeholder expectations and collaborate with cross-functional teams to align data solutions with business objectives.

Oversee the deployment and optimization of large-scale data pipelines to handle increasing data volumes effectively.

Requirements

A minimum of 8 to 12 years of professional experience in data engineering or related technical fields. Proven expertise in SQL and Python for data manipulation, scripting, and application development. Deep understanding of data modeling techniques and strategies for data optimization. Strong background in building, deploying, and managing ETL/ELT workflows and data integration solutions.

Demonstrated ability to monitor complex data pipelines and execute effective troubleshooting procedures. Exceptional stakeholder management skills with a track record of successful cross-departmental collaboration. Experience in leading teams, providing technical guidance, and conducting peer code reviews. Familiarity with the design and implementation of large-scale data infrastructure.