Lead Data Engineer

We are looking to hire a "Lead - Data Engineer" for one of our clients.

Qualifications

BSc in Information management, Data Analytics, Computer Science, or related field.
MSc in Data Analytics, Data Science, or related fields will be an advantage.
5+ years of experience in data engineering, with a focus on building and managing data pipelines, infrastructure and dashboards.
Solid understanding of ETL processes, data modelling, and data integration techniques.
Extensive experience with Azure Synapse, Azure Data Factory (ADF), Azure Databricks, and Power BI Dataflows, with a proven ability to architect solutions in these technologies.
Solid experience in cloud platforms (e.g., AWS, Azure) and containerisation (e.g., Docker, Kubernetes).
Good understanding of database technologies including RDBMS, DW/DL, Cloud, NoSQL and ELT/ETL with Data Warehouse solutions
Ability to work with multiple projects simultaneously.
Fluent English spoken and written
Strong presentation skills, as well as a business-building mindset, would be key advantages

Responsibilities

Lead the data engineering team in the design, development, and maintenance of data pipelines, ETL processes, and data integration solutions.
Architect, implement, test, deploy, and maintain stable, secure, and scalable data engineering solutions and pipelines to support various data projects and initiatives, utilizing Azure Data Fabric, Azure Data Factory (ADF), Azure Databricks, and Power BI Dataflows.
Ensure high standards of data quality, integrity, and security through robust data governance practices.
Collaborate with cross-functional teams to understand data requirements and deliver solutions that meet business needs.
Mentor and guide other junior engineers in the team on industry standards/ tools/ best practices in data engineering
Stay updated with the latest trends and technologies in data engineering and make recommendations for improvements.
Own Data Analytics and Insights Scrum teams and be responsible for the quality, timely deliveries.
Revisit the process and practices periodically to make necessary improvements to improve quality and efficiencies based on data/metrics.

If you are interested, please submit your CV to [email protected] or apply below.

See also