Specialist: Data Engineering at Telkom

Core Description    
Support data driven solutions by leveraging your software and data engineering experience across a wide set of use cases within the Telkom Group and throughout its customer base. In this role, you will become a member of Telkom Strategic Insights (TSI), a new team within Telkom Group’s Strategic Division; focused on maturing Telkom’s data landscape by building data driven solutions for large-scale problems. To succeed, you should be highly skilled in batch and real-time data ingestion, and data transformation with the ability to design, build and scale data pipelines for efficient data integration and processing. TSI is one of the biggest data orientated teams in the country, with over 50 data professionals spread over 4 smaller teams: Data Management, Data Insights, AI/ML, and Engineering. This team structure enables us to achieve a holistic balance between start-up and corporate culture.

Competencies    

• Extensive knowledge and experience with data engineering and integration concepts and terms.
• Strong relational and non-relational database foundational knowledge.
• Able to create and maintain robust, automated data pipelines and DAG workflows.
• Experienced with analysis and improvement of business and software systems and processes.
• Deep knowledge and understanding of best practices, standards and deployment options enabling data transfer in batches or real time, on-prem and in the cloud.
• Cloud platform management (GCP, Azure, AWS, etc.)
• Experience with Linux, Python 3, Java, Scala, SQL, JavaScript, and tools in our stack including: PySpark, Hadoop, Cloudera, Kafka, Airflow, Argo, Prefect, Dagster, Redis, RabbitMQ, Beam, Arrow, Arrow Flight, Iceberg, Dremio, Ab Initio, Tableau, Alteryx, PowerBI, GitLab, Terraform, GCP, PowerPoint, Word & Excel.
• Communication (written and verbal)
• Stakeholder management
• Skilled planner
• Takes initiative
• Growth mindset
• Can do attitude
• Systems orientated
• Innovative thinker
• Works well in teams
• Works well under pressure
• Relishes dynamic/changing environment
• Hard working and conscientious
• Highly skilled in multitasking and context switching
• Excellent time management
• Can solve difficult problems
• Strives to achieve high quality output

Job Responsibilities    

Data Engineering

• Architect, develop and maintain a declarative data integration solution with the ability to move data between databases, file systems and other locations while tracking data quality according to measures set by data owner.
• Enhance our big data and real-time data capabilities by using both open-source and internally developed tools, designed to efficiently move data while ensuring integrity.
• Oversee data support for specialized data centric services we provide, including ETL/ELT, data modelling, database design, data quality assessments, data format conversion, data transformation and data tool selection.
• Maintain up-to-date knowledge of data engineering tools, related technologies, data security and data privacy laws.
• Guide standards and best practices across the data stack.

Processes, Automation and DevOps

• Contribute to the development and maintenance of our toolsets used to automate various jobs and scheduled tasks.
• Mature our DevOps practices, cloud-based toolsets, and production applications.
• Identify systemic inefficiencies, conceptualise possible solutions and drive their development.
• Ensure consistent documentation of all implemented tools, systems, and processes.
• Support tools, data integration applications and infrastructure lifecycles via standard service management principles and processes.

Business and Leadership

• Use data engineering techniques to innovate and solve problems, translating business requirements into system designs.
• Engage with stakeholders to support the design and delivery of data science projects and solutions.
• Lead and develop a team of junior data engineers.
• Contribute to our agile way of work and innovation culture.

Required Certification    

• Any data or cloud platform (GCP, Azure, AWS) certification is required, other relevant certifications will be highly advantageous.

Qualifications    

• A formal qualification of at least NQF level 6 in Computer Science, Mathematics, Statistics, Software and/or Data Engineering or a related field. Any relevant specialised certifications or a post-graduate degree will be especially advantageous.

Experience    

• 3-5 years relevant experience, of which at least 2 years must have been in a data engineering environment.

Special Requirements    

• Experience with PySpark and other big data technologies
• Conscious of the various laws and privacy concerns surrounding personal and corporate data.
• Experience with task and workflow automation, DAGs, and relevant tooling.
• Experience with OOP and automated testing in Python.
• Experience with Google Cloud Platform and its products.
• Comfortable with mono-repo architecture and the relevant tooling.
• Experience with git workflows and interfaces.
• Experience with semantic versioning.

Closing Date: 25th, April 2022