Job Description: Key Responsibilities:

Leadership and Strategy

• Develop and implement the data engineering strategy aligned with the company’s business goals.
• Lead, mentor, and grow a high-performing data engineering team.
• Foster a culture of innovation, collaboration, and continuous improvement within the team.

Data Architecture and Infrastructure

• Design and oversee the implementation of robust, scalable, and efficient data architectures.
• Ensure the data infrastructure supports current and future data needs, including data warehousing, data processes, and big data technologies.
• Evaluate and implement new data technologies and tools to improve data processes and infrastructure.

Data Management

• Oversee the development and maintenance of data pipelines to ensure data quality and integrity.
• Implement best practices for data management, including data governance, data security, and data privacy.
• Ensure compliance with relevant data regulations and standards.

Collaboration and Communication

• Collaborate with data scientists, analysts, and business stakeholders to understand data needs and deliver appropriate solutions.
• Communicate complex technical concepts and solutions to non-technical stakeholders effectively.
• Act as a liaison between the data engineering team and other departments, promoting data-driven decision-making.

Performance and Optimization

• Monitor and optimize the performance of data systems to ensure high availability and reliability.
• Develop metrics and KPIs to measure the effectiveness of data engineering initiatives.
• Identify and resolve performance bottlenecks in data processes and systems.

Project Management

• Lead data engineering projects from conception to completion, ensuring they are delivered on time and within budget.
• Manage project resources, timelines, and deliverables, coordinating with other teams as necessary.
• Ensure thorough documentation of data systems, processes, and projects.

Responsibilities: Technical Skills:

• Proficiency in data engineering technologies and tools (e.g., Hadoop, Spark, Kafka, SQL, NoSQL databases).
• Experience with cloud platforms (e.g., preferable Google Cloud).
• Strong knowledge of ETL processes, data warehousing, and data architecture.
• Familiarity with data governance, data security, and compliance standards.
• Experience in big data technologies and real-time data processing.
• Knowledge of machine learning and AI principles and added advantage.
• Experience with data visualisation tools and techniques

Qualifications: Qualifications

• Education: Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Science, or a related field.
• Experience:
• Minimum of 15+ years of experience in data engineering or related roles.
• At least 5 years of experience in a leadership or managerial position.