Log In
Sign Up
At PwC, our people in data management focus on organising and maintaining data to enable accuracy and accessibility for effective decision-making. These individuals handle data governance, quality control, and data integration to support business operations. In data architecture at PwC, you will focus on designing and implementing the structure and organisation of data assets within the organisation. Your work will involve developing data models, defining data standards, and establishing data integration frameworks. You will assist in confirming that data is stored, accessed, and utilised efficiently, enabling seamless data flow across systems and supporting effective data management practices.
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.
Skills
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.
The Data Specialist helps the organization collect, organize, and prepare data so teams can use it for reporting, insights, and decision-making. This role works closely with business partners and technical teams to understand needs, deliver dependable data solutions, and make sure the information is accurate, secure, and available when needed.
- Technical Knowledge
- Strong experience in developing and supporting ETL/ELT pipelines in Databricks using Python (including PySpark) and SQL. Comfortable with scheduling/orchestration, parameterized runs, basic error handling and logging, and improving performance when jobs slow down. Familiar with Microsoft Fabric (e.g., Lakehouse/Warehouse, Hands on with Data Factory) and able to use Power Automate to streamline simple – complex operational steps and notifications.
- Strong working knowledge of Power BI, including supporting datasets/semantic models for reporting, creating and validating measures (DAX) as needed, and applying access controls such as row-level security. Partners with analysts to ensure dashboards are accurate, consistent, and perform well.
- Understanding of core data practices, including basic data modeling (e.g., dimensional/star models), data quality checks and reconciliations, and maintaining clear definitions and lineage for key datasets. Follows governance, privacy, and security expectations across raw, curated, and reporting layers.
- Soft skills
- Works independently to deliver end-to-end solutions, proactively unblocking progress and escalating risks with clear options and recommendations.
- Takes ownership of end to end deliverables from design through production, including documentation, monitoring/alerting, and continuous improvement.
- Demonstrates initiative by identifying opportunities
- Good understanding of technical / non-technical requirement gathering.
- Collaborates effectively with technical / non-technical teams
- Excellent communication skills.
- Builds trusted relationships with business stakeholders by setting expectations, validating definitions, providing status updates, and ensuring delivered datasets/reporting meet agreed acceptance criteria.
