Senior Data Engineer

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:

• 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.

2. 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.
• 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.

4. 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.

5. 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.
• 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.

7. 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.