Responsibilities of Data Engineering:
- Lead and manage the data engineering team, ensuring high performance and smooth workflow.
- Design, construct, install, test, and maintain highly scalable data management systems.
- Develop and implement data pipelines, ensuring robustness and data quality.
- Collaborate with data scientists and architects on several projects.
- Use Azure Databricks for big data analytics and processing.
- Implement ETL processes and work closely with the DevOps team to automate them.
- Use SQL and Python for data querying and analysis.
- Implement data visualization tools and techniques to present data in a way that is easily understandable for non-technical team members.
- Use Microsoft Fabric for data integration and processing.
- Apply strong analytical thinking to interpret data and make strategic decisions.
- Ensure data privacy and comply with all relevant data protection regulations.
- Continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for secondary datasets.
- Provide technical expertise and recommendations in assessing new IT software projects and initiatives to support and enhance our existing systems.
- Conduct team meetings and provide training to team members as necessary.
- Stay updated with the latest industry trends and technologies to ensure the company's data infrastructure is up-to-date.
The Data Engineering Lead should be a strategic thinker, excellent team leader, and a problem-solver with a strong analytical mindset. The ability to manage and interpret large amounts of data is crucial for this role.
Functional Competencies:
- Data Engineering: The candidate should have a strong understanding of database structures, theories, principles, and practices. They should be able to design, construct, install, test, and maintain highly scalable data management systems.
- Data Visualization: The candidate should be proficient in transforming data into readable, goal-driven reports for continued innovation and growth. They should be able to use data visualization tools to present data in a clear, concise manner.
- SQL: The candidate should have a strong understanding of SQL database coding and management. They should be able to write and optimize SQL statements for data access and retention.
- Python: The candidate should have experience in using Python for creating high-performing data processing scripts. They should be proficient in Python libraries for data analysis.
- Azure Databricks: The candidate should have experience in using Azure Databricks for big data analytics and artificial intelligence solutions.
- DevOps: The candidate should have a good understanding of DevOps principles and tools, and should be able to work in a fast-paced environment with a focus on continuous delivery.
- Data Pipeline: The candidate should have experience in building and maintaining reliable and scalable data pipelines to meet business needs.
- ETL: The candidate should have experience in designing, building, and maintaining ETL pipelines.
Behavioural Competencies:
- Team Management: The candidate should have strong leadership skills and the ability to manage a team effectively. They should be able to delegate tasks, motivate team members, and resolve conflicts.
- Analytical Thinking: The candidate should have strong analytical skills and the ability to make data-driven decisions. They should be able to analyze complex data sets and derive meaningful insights.
- Data Analysis: The candidate should have strong data analysis skills. They should be able to analyze large amounts of raw information to find patterns and use data-driven insights to help the company make business decisions.
Good to have skills:
- Microsoft Fabric: The candidate should have a deep understanding of Microsoft Fabric and its use in building scalable and reliable applications. They should be able to use Microsoft Fabric to design and implement high-quality solutions.