Data Engineer - Manager

You'll be joining our Data & AI capability as a Manager in the Data Engineering team, leading one or more teams to design and deliver advanced data solutions that address complex challenges for PwC and its clients. Operating at the forefront of data engineering, you'll support projects across various industries such as healthcare and financial services, shaping and scaling data platforms that underpin analytics, AI and machine learning.

You'll combine hands-on technical delivery with team leadership, setting direction for robust, modern data engineering practices. You'll work in a cross-functional environment, collaborating across business and technology to deliver tangible value from data.

We're looking for a motivated self-starter, comfortable with ambiguity and experienced in managing cross-functional delivery, with 4+ years of data engineering experience, to join us in either our Manchester or Birmingham offices.

What your days will look like:

• Leading and developing teams of data engineers, creating a collaborative, high-performing environment focused on building reliable and scalable data solutions.
• Providing technical direction for the design, build and support of data pipelines, data platforms and analytics infrastructure, ensuring alignment with organisational goals and industry best practices.
• Contributing hands-on to solution design, development and troubleshooting, including code reviews and resolution of complex technical issues.
• Building data engineering capability by driving adoption of modern techniques, tools and patterns, supporting the professional growth of your teams and the wider Data & AI capability.
• Engaging stakeholders across business, technology partners and clients to understand requirements, set priorities and deliver impactful data solutions.
• Ensuring quality by overseeing the development, deployment and validation of data solutions, maintaining high standards of accuracy, reliability and performance.

• Proven experience leading or managing data engineering teams or workstreams in complex environments.
• Strong object-oriented Python skills for developing, testing and packaging code, including experience with tools such as GitCliff, and familiarity with frameworks such as PyTorch and TensorFlow where relevant to data and AI workloads.
• Experience with Apache Spark for large-scale data processing.
• Effective use of coding tools such as Cursor, GitHub Copilot and similar to accelerate high-quality delivery.
• Experience developing APIs using FastAPI or similar technologies to expose data and analytics services.
• Strong understanding of business intelligence needs and optimising data transformations for AI and BI applications.
• Solid understanding of best practices in data engineering architecture, including data modelling, orchestration, testing and observability.
• Familiarity with SDLC methodologies such as SAFe, Agile and JadX and experience applying them to data engineering projects.
• Experience using repositories and DevOps tooling including GitHub and Azure DevOps.
• Hands-on experience with major data engineering tools and platforms such as Databricks, Microsoft Fabric, Azure Data Factory and Palantir.
• Experience with at least one major cloud platform (Azure, AWS or GCP), ideally more than one, for data engineering workloads.

PwC provides services to 420 out of 500 Fortune 500 companies. The firm was formed in 1998 by a merger between Coopers & Lybrand and Price Waterhouse.