At a Glance
- Tasks: Design and build scalable data solutions using modern technologies.
- Company: Join Accenture, a leader in innovation and technology services.
- Benefits: Hybrid working, competitive salary, and opportunities for continuous learning.
- Other info: Dynamic role with excellent career growth and a focus on well-being.
- Why this job: Shape large-scale data solutions and make a real impact in a collaborative environment.
- Qualifications: 3+ years in data engineering with strong Java or Python skills.
The predicted salary is between 60000 - 80000 £ per year.
Salary: TBC – Depending on experience
Hybrid Working: 3 days per week in our Newcastle, Cobalt business park office
Please Note: Any offer of employment is subject to satisfactory BPSS and SC security clearance which requires 5 years continuous UK address history (typically including no periods of 30 consecutive days or more spent outside of the UK) and declaration of being a British or EU passport holder or hold Indefinite Leave to remain within the UK at the point of application.
Our Advanced Technology Centre is a hub of innovation where we deliver high‑quality data and technology services to clients across both the public and private sectors. You’ll join a collaborative culture that values diverse thinking, continuous learning, and opportunities for career growth within a global network of experts.
If you're looking for a dynamic role that offers hands‑on experience with modern data technologies and the chance to shape large‑scale data solutions, this position offers you the opportunity to develop and progress rapidly.
As a Data Engineer, you will design, build, and maintain scalable data solutions that enable analytics, AI, and operational insights. You’ll work alongside client and internal teams to create robust data pipelines, ensure data reliability, and support cloud‑based architectures that power intelligent decision‑making.
- Data Pipeline Development
- Build, optimize, and maintain scalable data pipelines using Java (primary), plus exposure to Python, Flink, Kafka, or Spark.
- Integrate data from multiple sources (streaming, batch, APIs) using AWS managed services.
- Data Architecture & Standards
- Contribute to data modelling, data architecture best practices, and modern patterns.
- Ensure data quality, lineage, governance, and security controls are applied consistently.
- DevOps & Deployment
- Deploy and maintain data applications using CI/CD tooling (Azure DevOps, GitHub Actions, Jenkins).
- Work closely with analytics, ML/AI, and product teams to deliver clean, well‑structured datasets.
- Core Data Engineering
- Strong programming proficiency in Java (preferred) or Python.
- Understanding of software engineering best practices: testing, design patterns, CI/CD, Git.
- Experience building ETL/ELT or streaming data pipelines.
- Experience working with cloud platforms, ideally AWS, but Azure/GCP also acceptable.
- Understanding of distributed compute, large-scale data systems, and performance considerations.
- DevOps & Engineering Practices
- Experience with CI/CD tools (Azure DevOps, GitHub Actions, Jenkins etc.).
- Exposure to enterprise data platforms (Databricks, Snowflake, BigQuery, or similar).
- Cloud certifications (AWS, Azure, GCP) are beneficial but not required.
- Minimum 3 years’ experience working on data engineering or large-scale data solutions.
- Comfortable working in Agile delivery teams.
- Professional cloud or data engineering certifications.
Every day, more than 775,000 of us help our stakeholders continuously reinvent. Together, we drive positive change and deliver value to our clients, partners, shareholders, communities, and each other.
At Accenture, we see well-being holistically, supporting our people’s physical, mental, and financial health. We also provide opportunities to keep skills relevant through certifications, learning, and diverse work experiences.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Engineer, Data Engineering in Newcastle upon Tyne
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Accenture!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Senior Engineer, Data Engineering at Accenture.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Accenture.
✨Apply Directly through Our Website
When you find a suitable opening like Senior Engineer, Data Engineering at Accenture, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Senior Engineer, Data Engineering in Newcastle upon Tyne
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Accenture, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Accenture. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Accenture
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Accenture!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
