At a Glance
- Tasks: Lead teams to design and deliver advanced data solutions for diverse industries.
- Company: Join PwC's innovative Data & AI capability with a collaborative culture.
- Benefits: Flexible working, private medical cover, and six volunteering days annually.
- Other info: Dynamic environment with opportunities for professional growth and development.
- Why this job: Shape the future of data engineering while making a real impact.
- Qualifications: 4+ years in data engineering, strong Python skills, and team leadership experience.
The predicted salary is between 60000 - 80000 € per year.
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 and 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.
This Role Is For You If
- 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 GitLab, 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.
What You’ll Receive From Us
No matter where you may be in your career or personal life, our benefits are designed to add value and support, recognising and rewarding you fairly for your contributions. We offer a range of benefits including empowered flexibility and a working week split between office, home and client site; private medical cover and 24/7 access to a qualified virtual GP; six volunteering days a year and much more.
Data Engineer - Manager in Manchester employer: PwC UK
PwC is an exceptional employer that fosters a collaborative and high-performing work culture, particularly in our Manchester and Birmingham offices. As a Data Engineer Manager, you will not only lead innovative data engineering teams but also benefit from a comprehensive range of perks, including flexible working arrangements, private medical cover, and opportunities for professional growth through hands-on project involvement and continuous learning. Join us to make a meaningful impact across diverse industries while enjoying a supportive environment that values your contributions.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer - Manager in Manchester
✨Tip Number 1
Network like a pro! Reach out to your connections in the data engineering field, especially those at PwC. A friendly chat can sometimes lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data engineering projects. Whether it’s a GitHub repo or a personal website, having tangible examples of your work can really impress potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice common data engineering scenarios and be ready to discuss how you’ve led teams and tackled complex challenges in the past.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Data Engineer - Manager in Manchester
Some tips for your application 🫡
Tailor Your CV:Make sure your CV speaks directly to the role of Data Engineer - Manager. Highlight your experience in leading teams and delivering data solutions, and don’t forget to mention your technical skills with Python, Apache Spark, and cloud platforms!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to tell us why you’re passionate about data engineering and how your background aligns with our needs. Be sure to mention specific projects or achievements that showcase your leadership and technical expertise.
Showcase Your Technical Skills:We want to see your hands-on experience! Include examples of your work with data pipelines, APIs, and any tools like GitLab or Azure DevOps. This is your opportunity to demonstrate your problem-solving skills and technical know-how.
Apply Through Our Website:Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at PwC UK
✨Know Your Tech Inside Out
Make sure you’re well-versed in the technical skills mentioned in the job description, especially Python, Apache Spark, and data engineering tools like Databricks. Brush up on your coding skills and be ready to discuss your experience with frameworks like PyTorch and TensorFlow.
✨Showcase Your Leadership Skills
Since this role involves managing teams, prepare examples of how you've successfully led data engineering projects or teams in the past. Highlight your ability to create a collaborative environment and how you’ve driven team performance.
✨Understand the Business Context
Familiarise yourself with the industries PwC operates in, such as healthcare and financial services. Be prepared to discuss how your data solutions can deliver tangible value and meet business intelligence needs.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities in complex environments. Think of specific challenges you've faced in previous roles and how you overcame them, particularly in relation to data pipelines and analytics infrastructure.
