Qualifications Bachelors degree in Computer Science, Information Systems, Engineering, or a related field (required) Relevant certifications in data engineering, cloud platforms, or database technologies (advantageous) Experience & Skills 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). Technical Knowledge & Skills Core Stack Required 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. Additional Technologies Advantageous 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. Core Data Engineering Skills 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. Skills & Competencies 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.