Intermediate Data Engineer

Qualifications

• Bachelor’s degree in Computer Science, Information Systems, Engineering, or a related field (required)
• Relevant certifications in data engineering, cloud platforms, or database technologies (advantageous)

• Minimum 3 – 6 years of experience in data engineering or a closely related field.
• Proven hands-on experience with Databricks and Python in a production data engineering environment.
• Demonstrable experience with ETL/ELT tools and frameworks, particularly Informatica / IDMC.
• Experience configuring and maintaining monitoring and observability tooling (e.g., Grafana).
• Solid experience with SQL and relational database environments.
• Experience working with cloud platforms (AWS or Azure) is advantageous.
• Proven experience in the telecommunications or fibre industry is strongly preferred; candidates with relevant experience in other data-intensive industries will also be considered.
• Familiarity with OSS/BSS data environments and telecom data structures is advantageous.
• Knowledge of data governance, data security, and regulatory compliance (e.g., POPIA).

• Databricks – hands-on experience building and managing data pipelines, notebooks, and Delta Lake artefacts.
• Python – proficient in Python for data transformation, pipeline orchestration, and automation tasks.
• Informatica / IDMC – experience designing and maintaining data integration workflows and mappings.
• Grafana – ability to configure and interpret monitoring dashboards for pipeline observability.

• PowerBI – working knowledge of how PowerBI consumes and models data; ability to publish datasets and support report development is an advantage.
• Relational databases: PostgreSQL and/or Microsoft SQL Server.
• Cloud platforms: AWS or Microsoft Azure.
• Microsoft 365 / SharePoint – familiarity with data stored in or shared via Microsoft collaboration tools.

• Strong proficiency in SQL for data querying, transformation, and analysis.
• Solid understanding of data structures, data modelling, and database design principles.
• Experience with data quality management, data governance, and data security practices.
• Familiarity with data warehouse and data lake concepts, including layered architectures and dimensional modelling.
• Practical understanding of batch and/or streaming data processing patterns.

• Strong analytical and problem-solving skills with close attention to detail.
• Effective communication skills, with the ability to engage both technical and non-technical stakeholders.
• Ability to work collaboratively within a cross-functional data team in a fast-paced environment.
• Proactive approach to identifying and resolving data quality and pipeline issues.