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Data Engineer at PwC in Johannesburg
Job Description
Data Engineer
Location: Johannesburg
Management Level
Senior AssociateJob Description & Summary
At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth.In data engineering at PwC, you will focus on designing and building data infrastructure and systems to enable efficient data processing and analysis. You will be responsible for developing and implementing data pipelines, data integration, and data transformation solutions.
Focused on relationships, you are building meaningful client connections, and learning how to manage and inspire others. Navigating increasingly complex situations, you are growing your personal brand, deepening technical expertise and awareness of your strengths. You are expected to anticipate the needs of your teams and clients, and to deliver quality. Embracing increased ambiguity, you are comfortable when the path forward isn’t clear, you ask questions, and you use these moments as opportunities to grow.
Examples of the skills, knowledge, and experiences you need to lead and deliver value at this level include but are not limited to:
- Respond effectively to the diverse perspectives, needs, and feelings of others.
- Use a broad range of tools, methodologies and techniques to generate new ideas and solve problems.
- Use critical thinking to break down complex concepts.
- Understand the broader objectives of your project or role and how your work fits into the overall strategy.
- Develop a deeper understanding of the business context and how it is changing.
- Use reflection to develop self awareness, enhance strengths and address development areas.
- Interpret data to inform insights and recommendations.
- Uphold and reinforce professional and technical standards (e.g. refer to specific PwC tax and audit guidance), the Firm's code of conduct, and independence requirements.
Role Overview:
The Data Engineer is responsible for the development and support of our centralised, enterprise data workflows. These workflows automate the sourcing, transformation, and provisioning of data for consumers such as global and local applications, data models and reporting.
A Data Engineer has proven skills, ability and a delivery track record over several years and is able to work independently as well as within teams as a subject matter expert. Depending on the level of seniority, the engineer will be given responsibility for more technically challenging projects with greater financial impact and risk to the firm.
Key Responsibilities:
Data Pipeline Development
- Building and maintaining data pipelines that collect data from source systems, store the data within the Africa Data Warehouse, and process the data efficiently.
- Build data end points that provision data for consumers and applications, both locally and globally.
- Ensure that provisioned data is standardised and consistent in line with the enterprise data catalogue definitions.
- Ensure that deployment of code is done through approved DevOps pipelines and follows the development, staging and production lifecycle.
- Ensure ongoing review and maintenance of pipelines to meet changing business and data rules and requirements, and run efficiently.
Data Transformation
Converting of raw data into formats that meet consumer requirements and/or are useful for analysis, that may involve cleaning and wrangling the data. This includes:
Data Cleaning: Removing or correcting errors, inconsistencies, and duplicates in the data to ensure its accuracy and reliability.
Data Normalization: Standardizing data formats and structures to ensure consistency across different datasets.
Data Enrichment: Enhancing data by adding relevant information from external sources, which can provide more context and value.
Data Aggregation: Summarizing detailed data into higher-level insights, such as calculating averages, totals, or other statistical measures.
Data Integration: Combining data from various sources into a unified dataset, making it easier to analyse and derive insights.
Data Formatting: Converting data into the required format for analysis or reporting.
Ensuring that standard calculations are applied in line with data catalogue definitions.
Ensuring ongoing review of transformation calculations to align with changing business and data rules.
Data Modelling
Designing, constructing, and managing data models to ensure data can be referenced and related in a manner that supports business operations and processes.
Creating high-level (Conceptual) models that outline the overall structure of the data and how different data elements relate to each other.
Developing detailed (Logical) models that define the data elements, their attributes, and the relationships between them.
Translating logical models into physical models that specify how the data will be stored in databases, including tables, columns, data types, and indexes.
Mapping out how data moves through different systems and processes within the organization using data flow diagrams.
Data Quality Assurance
Ensuring the accuracy and integrity of data by developing validation methods and monitoring data quality.
Ensure that all deployed workflows meet the Definition of Done and have undergone peer review before being deployed to staging and production environments.
Ensure that all data deliverables meet the business requirements as outlined in the acceptance criteria.
Ensure robust, optimised data solutions by applying techniques such as stress testing.
Ensure that test tasks and results are recorded and confirmed.
Security and Compliance
Understanding of data governance and security policies and the application of secure coding principals to protect sensitive information.
Adherence to PwC data design and development standards.
Ensuring that accurate technical documentation is maintained.
Ensuring that security by design is implemented.
Ensuring that the concept of least privileged access to data and minimisation of data is applied.
Ensuring that data privacy techniques are applied such as data anonymisation, aggregation and de-identification.
Ensuring that data is encrypted in transit and at rest.
Ensure that appropriate change and release processes are followed.
Collaboration
Working with product teams and LoS stakeholders to understand data needs and ensure data is accessible and usable.
Attend design session to provide input into data requirements for solutions.
Develop technical designs and technical steps to meet data requirements and provide timelines that feed into overall project delivery.
Engage with local and global technical teams to determine dependencies and incorporate timelines and dependency steps into delivery considerations and timelines.
Technical Mentorship and Training
Act as a mentor to junior staff within the team.
Provide input into the development of technical training curriculums.
Provide technical input into data communities of interest and practice.
Desired Skills and Experience:
Strong analytical skills to troubleshoot and optimize data processes.
Ability to collaborate with data scientists, analysts, and other LoS stakeholders to understand data needs and convey technical concepts clearly.
Ability to ensure data accuracy and integrity through meticulous validation and monitoring.
Technical skills:
SQL
T-SQL
SSIS
SSAS
Database design
Database security
Database tuning
Database monitoring
Task automation/scheduling
Data modeling
Azure Data Lake
Azure SQL (serverless, memSQL)
Azure Synapse (Pipelines, SQL)
API development (SOAP, JSON, Graph)
Python (Pyspark)
Power Bi
Machine learning (understanding)
Containerisation (understanding)
Databricks(Working knowledge)
Travel Requirements
Up to 20%Available for Work Visa Sponsorship?
NoJob Posting End Date
August 21, 2026