Data Engineering Supervisor

Data Engineering Team Leader / Supervisor

Role Overview

The Data Engineering Team Leader / Supervisor will oversee the day-to-day development, implementation, and optimization of data engineering solutions and pipelines.

This role will lead a small team of approximately 3–5 Data Engineers while remaining hands-on in designing, developing, and maintaining scalable data platforms that support business intelligence, analytics, and reporting requirements.

The successful candidate should have strong technical expertise in Microsoft Azure data services, Databricks, ETL/ELT processes, and modern data engineering practices, combined with the ability to guide team members, manage deliverables, and ensure the quality and reliability of data solutions.

Responsibilities

1. Team Leadership & Supervision

• Lead, mentor, and provide technical guidance to a team of approximately 3–5 Data Engineers.
• Oversee daily team activities, workload distribution, task prioritization, and project deliverables.
• Monitor team performance, provide constructive feedback, and support the professional development of team members.
• Promote collaboration, accountability, technical excellence, and continuous learning within the team.
• Establish and implement data engineering standards, coding practices, and development guidelines.
• Conduct technical reviews and ensure the quality and consistency of the team's deliverables.

2. Data Engineering & Technical Development

• Design, develop, maintain, and optimize scalable data pipelines and data integration solutions.
• Support the development and maintenance of enterprise data platforms, including Data Lakes, Data Warehouses, and cloud-based analytics environments.
• Develop and manage ETL/ELT workflows to ensure efficient and reliable data processing.
• Work with Microsoft Azure data services, including Azure Data Factory, Azure Databricks, Azure Data Lake Storage, Azure Synapse Analytics, and related data services.
• Apply modern data engineering practices and automation techniques to improve pipeline performance and operational efficiency.
• Troubleshoot technical issues and ensure the availability, reliability, and scalability of data infrastructure.
• Remain hands-on in technical development, solution design, and complex problem-solving when required.

3. Data Quality & Governance

• Implement data quality standards, validation processes, and monitoring controls.
• Ensure the accuracy, completeness, consistency, and reliability of data across various platforms.
• Monitor and resolve data pipeline issues, production incidents, and performance bottlenecks.
• Collaborate with Data Governance and Information Security teams to ensure compliance with established data management policies.

4. Project & Stakeholder Coordination

• Work closely with Data Architects, Business Intelligence teams, Data Analysts, and other IT stakeholders to understand business requirements and translate them into technical solutions.
• Coordinate the delivery of data engineering projects, ensuring timelines, technical requirements, and quality standards are met.
• Provide technical recommendations and support for data platform enhancements and modernization initiatives.
• Communicate project progress, technical issues, and potential risks to relevant stakeholders.

5. Analytics & Continuous Improvement

• Support analytics, reporting, and AI/ML initiatives by developing reliable and accessible data assets.
• Identify opportunities to improve data pipeline performance, data accessibility, and operational efficiency.
• Stay updated on emerging technologies, tools, and best practices in data engineering and cloud-based analytics.

Qualifications

• Bachelor's Degree in Computer Science, Information Technology, Computer Engineering, Mathematics, Statistics, or a related field.
• Minimum of 5–8 years of experience in Data Engineering, Data Warehousing, Business Intelligence, or related fields.
• At least 1–3 years of experience in a Team Lead, Technical Lead, or Supervisory capacity, preferably handling Data Engineers or related technical teams.
• Experience directly leading, supervising, or mentoring a small team of approximately 3–5 technical professionals is preferred.
• Strong hands-on experience with Microsoft Azure Cloud Services, particularly Azure Data Factory, Azure Databricks, Azure Data Lake Storage, Azure Synapse Analytics, and related data services.
• Solid technical knowledge of Databricks, Data Lakes, Data Warehouses, and ETL/ELT processes.
• Strong proficiency in Python, PySpark, and SQL, including experience in large-scale data processing.
• Experience designing, developing, and maintaining enterprise-grade data pipelines and data integration solutions.
• Knowledge of data governance, data quality management, and master data management principles.
• Familiarity with data visualization and analytics platforms such as Power BI, Tableau, or Qlik.
• Ability to provide technical guidance, manage team deliverables, and coordinate with cross-functional stakeholders.
• Strong analytical, problem-solving, communication, and collaboration skills.
• Exposure to analytics, AI/ML, and digital transformation initiatives is an advantage.

We are prioritizing technically strong, hands-on Data Engineers who have experience leading or supervising a small team of 3–5 members, mentoring engineers, and overseeing data engineering deliverables.