Data Engineer at MultiChoice

Purpose of the Position

• To collect and validate business information (which can then be used by data scientists and business stakeholders).
• To design and implement data models and architectures that enable business stakeholders to gain insights from large-scale datasets.
• To design and develop ETL processes, pipelines, and data validation processes to ensure accuracy and consistency of data.
• To identify valuable data assets, in order to reduce duplication and enable faster delivery of deeper insights to the ST Operational reporting.
• Identify key drivers and value creators within user journeys.
• Developing and maintaining dashboards, reports, and visualizations that provide insights into key business metrics and trends.
• To help the organizations leverage their data to make informed decisions and improve business outcomes.

Key Performance Objectives

Compliance

• Ensure solutions adhere to standards and best practices and participate in solution reviews to ensure all solutions fit within standards
• Operate within project environments and participate in CII continuous improvement efforts

Data Engagement and Support

• Align to the division’s capability in using operational data to drive consolidated, reusable datasets, reducing data silos and duplication.
• Provide support to ST 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 and provide actionable insights for future planning and optimization.
• Maintain, build and improve data consolidation and automation from operational data to package data for re-use.
• 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 to Data Modeling and Database Design Standards.
• Align to 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.
• Provide subject matter expertise in the review of data sources, decoding data inconsistencies, and provide business logic for initiating data discovery.
• Collaborate with data science teams and build the right solutions for them.
• Assist in designing and develop data models, ETL processes, and pipelines to support data ingestion, transformation, and analysis.
• Develop and maintain dashboards, reports, and visualizations that provide insights into key business metrics and trends.
• Design and implement data quality and data validation processes to ensure accuracy and consistency of data.

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 year 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)