- Location
- Johannesburg, South Africa
- Type
- Full-time
- Department
- Engineering
- Seniority
- Senior
- Experience
- 8+ years
- Education
- Bachelor
- Closing date
- Today
- Source
- Workday
Description
Management Level
ManagerJob 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.
Enhancing your leadership style, you motivate, develop and inspire others to deliver quality. You are responsible for coaching, leveraging team member’s unique strengths, and managing performance to deliver on client expectations. With your growing knowledge of how business works, you play an important role in identifying opportunities that contribute to the success of our Firm. You are expected to lead with integrity and authenticity, articulating our purpose and values in a meaningful way. You embrace technology and innovation to enhance your delivery and encourage others to do the same.
Examples of the skills, knowledge, and experiences you need to lead and deliver value at this level include but are not limited to:
- Analyse and identify the linkages and interactions between the component parts of an entire system.
- Take ownership of projects, ensuring their successful planning, budgeting, execution, and completion.
- Partner with team leadership to ensure collective ownership of quality, timelines, and deliverables.
- Develop skills outside your comfort zone, and encourage others to do the same.
- Effectively mentor others.
- Use the review of work as an opportunity to deepen the expertise of team members.
- Address conflicts or issues, engaging in difficult conversations with clients, team members and other stakeholders, escalating where appropriate.
- 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.
Position Overview:
The Data Engineer is accountable for developing and maintaining the organisation’s centralised enterprise data workflows. These workflows facilitate the automated sourcing, transformation, and delivery of data to consumers including global and local applications, data models, and reporting functions. A Senior Data Engineer demonstrates advanced technical expertise, a strong track record of successful project delivery, and the ability to contribute both independently and as an expert within teams. Based on seniority, the engineer may be assigned responsibility for complex projects with significant financial implications and associated risk to the firm.
Culture Vision Statement:
Our Africa Tech team is a dynamic community of self-starters and innovators who embrace change and the constant evolution of technology. We are committed to fostering an environment where curiosity thrives and creativity is celebrated. By leveraging AI-assisted tools and respecting diverse perspectives, we drive collaborative success while enhancing our collective knowledge and skills. We believe in the power of technology to transform and improve our processes, always seeking to challenge the status quo. Together, we work with urgency and empathy, united in our mission to create impactful solutions that benefit our firm and our clients.
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.
Qualifications and Skills:
Education:
Bachelor Degree in Computer Science or equivalent relevant work experience.
Certifications in Microsoft Azure Synapse
Certifications in Microsoft Databricks.
Experience: Minimum of 8 - 10 years of experience in cloud data engineering or related roles within a complex organizational environment.
Technical Skills:
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
Databricks experience (ideal).
Data Mesh experience (ideal).
Data modeling
Azure Data Lake
Azure SQL (serverless, memSQL)
Azure Synapse (Pipelines, SQL)
API development (SOAP, JSON, Graph)
Python (Pyspark
Power Bi
Travel Requirements
Up to 20%Available for Work Visa Sponsorship?
NoJob Posting End Date
August 10, 2026
