Senior Data Engineer

Position Overview:

The Data Engineer is accountable for developing and maintaining the organisations 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

• 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)

Conclusion

At PwC our purpose is to build trust in society and solve important problems. As we navigate an increasingly complex world we are dedicated to ensuring that the systems on which communities and economies depend can adapt and thrive. Each role within our organization contributes to this mission reinforcing our commitment to high ethical standards and the importance of trust.

Our five core values guide our actions and define who we are. They emphasize building trust through professionalism ethical behavior and a commitment to quality in all our interactions whether with clients colleagues or the broader community. We respect privacy and confidentiality and we strive for transparency in our operations demonstrating care and integrity in our relationships.

As part of our human-led tech-powered approach we empower our people through technology and foster an environment were speaking up is encouraged and diverse perspectives are celebrated. By embodying these principles in our daily work we can collectively drive impactful outcomes that resonate with our clients and society as a whole. Together lets embrace our purpose and values to create meaningful change.

The Senior Data EngineerLead is essential in developing data models for provisioning ensuring trust in our data generating revenue through data monetisation and maintaining the quality and integrity of the organizations data assets. Your deep knowledge of data engineering practices and dedication to promoting a culture of accountability will greatly enhance the effectiveness of data provisioning initiatives within PwC Africa. A strong passion for data engineering cloud solution architecture and alignment with PwCs purpose are crucial for success in this role.