The Head of Data Engineering within a Data Analytics Centre of Excellence (COE) is a critical leadership position responsible for managing the design, development, and deployment of data engineering solutions that support the COE's data analytics capabilities. The role requires a deep understanding of data engineering technologies, data management practices, and analytical methodologies.

• Data Engineering Strategy: Develop and implement a data engineering strategy aligned with the COE's overall data analytics strategy, ensuring the architecture, tools, and technology stack align with the organization's goals.
• Team Management: Lead and manage a team of data engineers to deliver data engineering solutions that meet business needs, including hiring, mentoring, training, and performance management.
• Data Architecture: Develop and maintain the data architecture, including data models, data pipelines, data storage, and data integration, that supports the COE's data analytics capabilities.
• Technical Leadership: Provide technical leadership and guidance to the data engineering team and work closely with other teams within the COE to ensure alignment with the broader data analytics strategy.
• Data Quality: Ensure the quality, accuracy, and completeness of data used for analytics by establishing and enforcing data quality standards, data governance policies, and best practices for data management.
• Technology Evaluation: Evaluate and recommend new data engineering technologies, tools, and platforms that can enhance the COE's data analytics capabilities and improve operational efficiency.
• Collaboration: Foster a collaborative environment with other teams, such as data scientists and business analysts, to enable effective collaboration and support the delivery of high-quality analytics solutions.
• Performance Monitoring: Monitor and manage the performance of data engineering solutions, including identifying and addressing technical issues, optimizing performance, and ensuring scalability.

Minimum Qualifications:

Bachelor in Computer Engineering / Master’s in Computer Science, quantitative science discipline such as physics, mathematics. Should be hands on and have expert level competency in data manipulation, data management, data modelling, data visualization and data analytics and / or machine learning. (Mandatory)

Experience:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• At least 10 years of experience in data engineering, with at least 5 years of experience in a leadership role.
• Strong understanding of data engineering technologies and platforms, such as Hadoop, Spark, Kafka, and AWS.
• Experience with data modeling, ETL, data warehousing, and data integration.
• Excellent leadership and team management skills, with the ability to mentor and develop team members.
• Strong analytical and problem-solving skills, with the ability to work in a fast-paced, rapidly changing environment.
• Excellent communication and collaboration skills, with the ability to work effectively with other teams and stakeholders.
• Experience in a Data Analytics Centre of Excellence or similar organization is a plus.
• Experience with data lakes and distributed systems such as Hadoop and Spark, including knowledge of tools such as HDFS, YARN, and Hive.
• Strong experience with cloud-based data warehousing platforms, such as Amazon Redshift, Google BigQuery, or Snowflake.
• Experience with NoSQL databases such as MongoDB or Cassandra.
• Knowledge of containerization technologies such as Docker and Kubernetes.
• Familiarity with DevOps practices and tooling, including continuous integration and continuous delivery (CI/CD).
• Knowledge of machine learning and AI technologies and their applications to data engineering.
• Strong understanding of data security and encryption methodologies, including knowledge of security protocols such as SSL/TLS and OAuth.
• Experience with data streaming technologies such as Apache Kafka and Apache Flink.
• Familiarity with data virtualization and data federation technologies such as Denodo.
• Experience with data cataloging tools such as Alation, Collibra, or Informatica Axon.