Data Engineer at Kontak Recruitment

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