Data Engineer (Mid-level)

Wppmedia; Johannesburg; On-site; ZAR 700,000 - 1,000,000; Full time

Job summary

WPP Media South Africa is seeking a mid-level Data Engineer to design and maintain scalable ELT/ETL pipelines and robust data models that power analytics and reporting.

You will build integrations via REST APIs, manage cloud-based data warehousing, and ensure data quality with automated testing and governance practices. The role collaborates with Analytics, BI, and tech teams to deliver trusted datasets.

Qualifications

• Bachelor's Degree in Computer Science, Information Systems, Engineering, Data Science or a related quantitative discipline.
• Google Cloud Professional Data Engineer certification (preferred).
• Additional cloud or data engineering certifications are advantageous.

Responsibilities

• Design, develop and maintain scalable ELT/ETL pipelines using modern cloud technologies.
• Build and optimise data ingestion frameworks from first-party, second-party and third-party data sources.
• Develop and maintain robust data models that support analytics and reporting.
• Build and manage integrations using REST APIs, SDKs and cloud services.
• Develop reusable data transformation frameworks using dbt or equivalent tooling.
• Implement incremental processing, partitioning and performance optimisation techniques.
• Design and maintain data warehouse structures within BigQuery and other cloud environments.
• Ensure pipeline reliability through monitoring, alerting and automated testing.
• Troubleshoot production data issues and implement long-term solutions.
• Develop scalable cloud-native data solutions on Google Cloud Platform.
• Optimise BigQuery performance and manage cloud infrastructure costs.
• Build solutions using Cloud Functions, Cloud Run, Cloud Storage and Pub/Sub where appropriate.
• Implement secure access controls using IAM principles.
• Support infrastructure automation using Infrastructure-as-Code where applicable.
• Monitor platform performance and recommend improvements.
• Implement automated data validation and quality assurance processes.
• Develop and maintain metadata, documentation, and data lineage.
• Support data governance frameworks and taxonomy standards.
• Ensure compliance with data privacy, security, and governance policies.
• Establish monitoring for data freshness, completeness, and accuracy.
• Build curated datasets that enable self-service reporting and analytics.
• Partner with Analytics Engineers, BI Developers and Data Analysts to deliver trusted datasets.
• Support dashboard development by providing clean, scalable, and well-modelled data.
• Develop reusable business logic and transformation layers.
• Assist with complex data investigations and root cause analysis.

Skills

SQL (Advanced)

Python

REST APIs

JSON

Git

Education

Bachelor's Degree in Computer Science, Information Systems, Engineering, Data Science or related

Tools

dbt

ETL/ELT

Data Modelling

Data Warehousing

Incremental Processing

Pipeline Orchestration

Data Quality Frameworks

Identity Resolution concepts

Job description

About WPP Media

WPP is the trusted growth partner for the world’s leading brands. With exceptional talent, trusted data and intelligence, and world-class partnerships – all united by ourpioneeringagentic marketing platform, WPP Open – we help clients navigate change, capture opportunity, and deliver transformational growth.

WPP Media is WPP's AI-driven media operating unit, bringing together media, data, and partnerships to deliver creative personalisation at scale. Connected through WPP Open and powered by Open Intelligence, clients see exactly where, how, and why their media investment is working.

For more information, visit wppmedia.com.

Job Title: Data Engineer (Mid-Level)
Company: WPP Media South Africa
Location: Johannesburg, South Africa
Reports To: Senior Data Specialist

Key Responsibilities

Data Engineering

• Design, develop and maintain scalable ELT/ETL pipelines using modern cloud technologies.
• Build and optimise data ingestion frameworks from first-party, second-party and third-party data sources.
• Develop and maintain robust data models that support analytics and reporting.
• Build and manage integrations using REST APIs, SDKs and cloud services.
• Develop reusable data transformation frameworks using dbt or equivalent tooling.
• Implement incremental processing, partitioning and performance optimisation techniques.
• Design and maintain data warehouse structures within BigQuery and other cloud environments.
• Ensure pipeline reliability through monitoring, alerting and automated testing.
• Troubleshoot production data issues and implement long-term solutions.

Cloud Platform Engineering

• Develop scalable cloud-native data solutions on Google Cloud Platform.
• Optimise BigQuery performance and manage cloud infrastructure costs.
• Build solutions using Cloud Functions, Cloud Run, Cloud Storage and Pub/Sub where appropriate.
• Implement secure access controls using IAM principles.
• Support infrastructure automation using Infrastructure-as-Code where applicable.
• Monitor platform performance and recommend improvements.

Data Quality & Governance

• Implement automated data validation and quality assurance processes.
• Develop and maintain metadata, documentation, and data lineage.
• Support data governance frameworks and taxonomy standards.
• Ensure compliance with data privacy, security, and governance policies.
• Establish monitoring for data freshness, completeness, and accuracy.

Analytics Enablement

• Build curated datasets that enable self-service reporting and analytics.
• Partner with Analytics Engineers, BI Developers and Data Analysts to deliver trusted datasets.
• Support dashboard development by providing clean, scalable, and well-modelled data.
• Develop reusable business logic and transformation layers.
• Assist with complex data investigations and root cause analysis.

Engineering Best Practices

• Maintain source control using Git.
• Participate in code reviews and technical design discussions.
• Develop technical documentation for pipelines, architecture, and integrations.
• Contribute to CI/CD deployment processes.
• Continuously improve engineering standards and platform reliability.
• Research and evaluate emerging cloud and data engineering technologies.

Collaboration

• Work closely with Media, Analytics, Strategy and Technology teams.
• Collaborate with platform vendors and technology partners.
• Support junior engineers through knowledge sharing and mentoring.
• Contribute to continuous improvement initiatives across the data practice.

Minimum Requirements

Qualifications

• Bachelor's Degree in Computer Science, Information Systems, Engineering, Data Science or a related quantitative discipline.
• Google Cloud Professional Data Engineer certification (preferred).
• Additional cloud or data engineering certifications are advantageous.

Experience

• 3–5 years' experience in Data Engineering or Analytics Engineering.
• Experience developing cloud-based data platforms.
• Experience building scalable data pipelines.
• Experience integrating APIs and third-party platforms.
• Experience working with modern data warehouses.
• Experience supporting business intelligence and analytics platforms.
• Experience within media, marketing technology or digital analytics is advantageous.

Technical Skills

Programming

• SQL (Advanced)
• Python
• REST APIs
• JSON
• Git

Data Engineering

• dbt
• ETL / ELT
• Data Modelling
• Data Warehousing
• Incremental Processing
• Pipeline Orchestration
• Data Quality Frameworks
• Identity Resolution concepts

Cloud Platforms

• Google Cloud Platform
• BigQuery
• Cloud Storage
• Cloud Functions
• Cloud Run
• Pub/Sub
• Cloud Composer (Airflow)
• IAM

Advantageous:

• Snowflake
• AWS
• Azure
• Terraform
• Docker

Analytics and BI

• Looker
• Looker Studio
• Power BI
• Tableau
• Google Analytics 4
• Adobe Analytics (advantageous)

Marketing Technology (Advantageous)

Experience integrating or working with data from:

• Google Ads
• Campaign Manager 360
• DV360
• Meta Ads
• TikTok
• AppsFlyer
• SA360
• Adverity

Knowledge

• Modern Data Architecture
• Cloud Computing
• Data Governance
• Data Privacy
• Data Modelling
• Data Quality
• Data Observability
• CI/CD Principles
• Version Control
• Cost Optimisation
• Digital Marketing Ecosystem
• AI-enabled Data Engineering

Success Measures

The successful candidate will:

• Deliver reliable, scalable and secure data pipelines.
• Maintain high levels of data quality and platform reliability.
• Improve pipeline performance and reduce processing costs.
• Enable trusted reporting through well-modelled datasets.
• Deliver production-ready engineering solutions using best practices.
• Contribute to continuous improvements in the data platform.
• Produce clear technical documentation and reusable engineering assets.

Behavioural Competencies

• Strong analytical thinking
• Problem solving
• Attention to detail
• Curiosity and continuous learning
• Ownership and accountability
• Collaboration and communication
• Adaptability
• Innovation
• Mentoring and knowledge sharing

Ideal Candidate

The ideal candidate is passionate about building scalable cloud data platforms and solving complex data integration challenges. They have strong SQL and Python skills, experience working with modern cloud technologies, and enjoy developing reliable, maintainable data products that enable analytics across the organisation.

They are comfortable working with large-scale datasets, APIs, orchestration tools and cloud infrastructure, while collaborating with analysts, strategists and stakeholders to deliver high-quality data solutions. Experience within marketing technology, digital media or advertising ecosystems is advantageous but not essential.

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