Data Engineer (JB4681) Sandton, Johannesburg Market Related CTC
Permanent
Our client is a high-energy, fast-paced, and results-driven organisation with a strong focus on collaboration and innovation in the FinTech space!
The environment is both dynamic and supportive, offering employees the chance to engage with cutting-edge technology while continuously learning and growing.
This role is perfect for someone who is technically strong and ready to take ownership of the data warehouse while thriving in a fast-paced, high-energy environment.
Minimum Requirements: Post-matric technology qualification 3-6 years of Data Engineering and BI experience, this must include data extraction, transformation, and loading. Proficient in JavaScript, ETL processes, SQL and Python. Knowledge of business analysis processes. Strong communication, interpersonal skills and highly resourceful. Able to plan, organise and problem-solve. Service delivery and excellence-orientated. A high degree of self-management and initiative. Duties and Responsibilities: Data Management Agreeing with the analytics teams on the necessary data elements. Maintain a Data Dictionary and Business Glossary. Deep understanding of the Production system mechanics of each of our product flows. Understand how Salesforce is implemented. Database Objects, Fields, and Records. Their relationships and how data is created and modified in the system. Provide detailed information on database changes. Object, field and records updates. Formula calculations. Object relationships. Stakeholder Communication and Change Management Ensuring that the requirements of the data consumers are clearly understood by the technology teams. Ensuring that any changes to production data are clearly understood by the data consumers before they are made and that the impact of the change is managed well. Communicating across all departments within the company: Especially Credit and Technology Teams – Development, Operations and Delivery Data lake/warehousing management Understand how system enhancements, new features and bug fixes will impact the data warehouse. Implement new solutions and flows. Extracting and transforming data from different systems. Qualify and score data providing quality and accuracy reports on key data elements. Monitor the data warehouse ensuring all data flows are running as scheduled. Data Quality Management Moving data if required for updates on the system. Updating records with external data. Providing data stewards with agreed quality metrics on the elements for which they are responsible. Cleaning bad data when identified or working with the data stewards to do so. Data lake maintenance Agreeing with the analytics teams on the necessary data elements. Ensuring these elements are consistently updated from production.
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