Summary of Role
The purpose of the Data Engineer is to leverage their data expertise and data related technologies, in line with the Data Architecture Roadmap, to advance technical thought leadership for the Enterprise, deliver fit for purpose data products, and support data initiatives. In addition, Data Engineers enhance the data infrastructure of the to enable advanced analytics, machine learning and artificial intelligence by providing clean, usable data to stakeholders. They also create data pipelines, Ingestion, provisioning, streaming, self-service, API and solutions around big data that support the strategy to become a data driven organisation.
Responsibilities
Responsible for the day to day maintenance of data pipelines, providing support to the data squads including performing data related tasks (such as data profiling, data cleaning, data configurations, data support, data validation, data quality assurance) in a Data Epic and assisting in basic data pipelines, data ingestion and supporting Data Engineers in their Epics.
Data Infrastructure: Support and maintain the Data Infrastructure to ensure it is secure, available and reliable.
Data Pipeline Build (Ingestion, Provisioning, Streaming and API).
Maintain data pipelines starting with Data Virtualisation, then progressing to Data Ingestion and finally to Data Provisioning.
Ensure that data pipelines are monitored and run successfully and configure and support minor changes in data pipelines.
Data Visualisation: Create virtual data bases and assist in creating data extracts for the business in response to business needs.
Documentation and Data Analysis: Collaborate with the Data Analyst to perform data profiling, data validation, and data documentation in support of Epics.
Data Warehouse Monitoring and Support: Monitor data pipelines and infrastructure to provide first line support, resolve issues and ensure that the warehouse meets its SLA timelines for data availability and warehouse reliability.
Cloud Monitoring and Support Services: Ensure cloud processes (Compute and Storage) are monitored and managed daily, to ensure that Cloud pipelines run successfully.
Operations: Support and run daily operational reports to ensure that all jobs ran successfully and that the data warehouse is maintained according to standard.
Collaboration: Work collaboratively with business stakeholders to gain business knowledge, understand data extracts and improve and optimise business queries.
Qualifications
Matric and a Bachelor's degree in Computer Science, Information Systems, Engineering, or related field.
7 years of experience in data engineering or a related discipline.
Strong experience with SQL and relational database technologies.
Proficiency with ETL/ELT tools and frameworks (e.g., Airflow, DBT, Azure Data Factory, AWS Glue).
Experience with cloud platforms (Azure, AWS, or GCP).
Knowledge of big data technologies (e.g., Spark, Hadoop) is advantageous.
Experience with Python or Scala for data processing.
Skills
SQL, Python (required)
Cloud services (Azure/AWS/GCP)
Data warehousing (Snowflake, Redshift, BigQuery, Synapse)
Big data frameworks (Spark, Databricks)
Version control (Git)
Containerisation (Docker/Kubernetes)