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- Design and implement data models and architectures that provide insights from large-scale datasets.
- Develop ETL processes, pipelines, and data validation processes to ensure accuracy and consistency of data.
- Identify valuable data assets and reduce duplication to deliver deeper insights to Operational reporting.
- Help the organization leverage its data to improve business outcomes.
Data Engineer - Randburg - Golden Rule

Description
Job Description:
To collect and validate business information that can be used by data scientists and business stakeholders, enabling them to make informed decisions.
Data Engagement and Support
Align with the division's capability in using operational data to drive consolidated, reusable datasets, reducing data silos and duplication.
Provide support to Executive Team comprised of IT Operations, Development, and Program Management data.
Identify opportunities to improve business as usual processes using modern technologies and automation.
Work with cross-functional teams to identify data requirements, prioritize data initiatives, and drive solutions that enable data-driven decision making.
Analyze operational data performance and effectiveness, providing actionable insights for future planning and optimization.
Maintain, build, and improve data consolidation and automation from operational data to package data for reuse.
Design and implement complex enterprise view reports in existing IT Operational Reporting tools.
Analyze and organize raw data.
Technical Competency
Be accountable for solutions, their sustainability, and accuracy, as well as delivery deadlines.
Share knowledge and practical experience with community and team.
Implement best practice Data warehousing development based on methodology adopted for solution by Architecture.
Operational Delivery
Develop and comply with Data Modeling and Database Design Standards.
Align with Data Model and Database Design Quality.
Develop and Maintain Logical Data Models.
Develop and Maintain Physical Data Models.
Design Physical Databases.
Communicate with various business areas and gather and prioritize their business requirements.
Manage application and data integration platforms.
Create reliable pipelines.
Combine data sources.
Architect data stores.
Qualifications
Minimum suitable tertiary qualification or an equivalent.
Post graduate qualification would be advantageous.
Cloud Technology Certifications in Databricks, AWS, Azure, Data Engineering, and other technologies highly advantageous.
Experience
A minimum of 2-3 years in ETL Framework driven environment.
3-5 years Multiple database experience advantageous.
MSSQL/SQL, MySQL, Postgres SQL, No-SQL, Data Virtualization, AWS data.
At least 5 years working experience.
Knowledge of Data Virtualization and Data Warehousing.
Technical Competencies
Data Modeling and design.
Programming knowledge and skills.
Project Management.
Risk Management.
Customer Expectation Management.
Business Analysis Techniques.
BI and Analytics Architecture (including Big Data).
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