At a Glance
- Tasks: Design and build scalable data pipelines using modern technologies.
- Company: Join a dynamic team at a leading tech company focused on data solutions.
- Benefits: Enjoy competitive salary, ongoing mentoring, and career growth opportunities.
- Other info: Work with cutting-edge tools like Kafka, Flink, and cloud platforms.
- Why this job: Make an impact with your passion for data in a collaborative environment.
- Qualifications: 2+ years in data engineering with strong Java or Python skills.
The predicted salary is between 40000 - 48000 £ per year.
We are seeking a skilled and motivated Data Engineer with 2+ years of experience to design, build, and maintain scalable data pipelines and systems. You will play a key role in developing both batch and real-time data processing solutions, working with modern data technologies and cloud platforms. This role requires strong engineering fundamentals, a passion for data, and the ability to work collaboratively in Agile teams.
Key Responsibilities
- Design, build, and maintain scalable ETL/ELT and streaming data pipelines
- Develop robust and efficient data processing solutions using Java (preferred) or Python
- Implement and manage stream processing systems using tools like Kafka, Flink, or Spark
- Work with large-scale distributed systems, ensuring performance, scalability, and reliability
- Collaborate with cross-functional teams to deliver high-quality data solutions
- Apply software engineering best practices including testing, version control, and CI/CD
- Contribute to architecture discussions and help improve data platform capabilities
- Monitor, troubleshoot, and optimize data pipelines and systems
- Maintain clear documentation for data pipelines, systems, and processes
Requirements
Required Skills & Experience
- 2+ years of experience in data engineering or large-scale data systems
- Strong programming skills in Java (preferred) or Python
- Hands-on experience with at least one: Kafka, Flink, or Spark (Kafka/Flink preferred)
- Solid understanding of stream processing concepts (event time, state, backpressure)
- Experience building ETL/ELT pipelines or real-time streaming workflows
- Knowledge of distributed systems and microservices architecture
- Experience with cloud platforms (AWS preferred, Azure/GCP also acceptable)
- Good understanding of data processing performance and scalability considerations
- Familiarity with Git, CI/CD pipelines, and software engineering best practices
DevOps & Engineering Practices
- Experience using CI/CD tools (e.g., Azure DevOps, GitHub Actions, Jenkins)
- Infrastructure-as-Code experience (Terraform preferred)
- Familiarity with containerisation (Docker) and orchestration (Kubernetes/EKS)
Tools & Technologies (Preferred Exposure)
- Experience with modern data platforms such as Databricks, Snowflake, or BigQuery
- Exposure to cloud-native data services and data lake/warehouse solutions
Key Competencies
- Strong analytical and problem-solving skills
- Ability to work effectively in Agile delivery environments
- Excellent communication skills with both technical and non-technical stakeholders
- Proactive mindset with a focus on continuous improvement
Desirable Skills
- Experience in client-facing or consulting roles
- Exposure to real-time, low-latency systems
- Cloud or data engineering certifications (AWS, Azure, GCP)
- Experience mentoring or supporting junior team members
What You’ll Bring
- Passion for building scalable, high-performance data systems
- Strong engineering mindset and attention to detail
- Collaborative approach and willingness to learn and grow
Benefits
Why Join Us?
- Dedicated support: Ongoing mentoring and guidance from experienced professionals
- Upskilling opportunities: Training and development to further strengthen your skillset
- Career growth: Work on impactful projects and progress into more senior roles
- Collaborative culture: A team environment that values knowledge sharing and continuous improvement
- Competitive salary and benefits package
Apply Today!
If you’re ready to take the next step in your career and grow with the support of nology and a dedicated team, submit your application now. We look forward to hearing from you!
Data Engineer in Newcastle upon Tyne employer: _nology
At nology, we pride ourselves on being an excellent employer, offering a collaborative culture that fosters continuous improvement and knowledge sharing. Our Data Engineers benefit from dedicated support and ongoing mentoring, alongside ample opportunities for upskilling and career growth within a dynamic team environment. Join us to work on impactful projects while enjoying a competitive salary and benefits package in a location that champions innovation and professional development.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer in Newcastle upon Tyne
✨Network Like a Pro
Get out there and connect with people in the industry! Attend meetups, webinars, or even just grab a coffee with someone who’s already in the data engineering field. You never know when a casual chat could lead to your next job opportunity.
✨Show Off Your Skills
Don’t just tell us what you can do; show us! Create a portfolio of projects that highlight your experience with ETL/ELT pipelines, streaming data solutions, or any cool stuff you've built using Java or Python. This will make you stand out from the crowd.
✨Ace the Interview
Prepare for those interviews by brushing up on your technical skills and understanding of data processing concepts. Be ready to discuss your past projects and how you tackled challenges. Remember, we love a proactive mindset and a collaborative spirit!
✨Apply Through Our Website
When you find a role that excites you, apply through our website! It’s the best way to ensure your application gets the attention it deserves. Plus, we’re always looking for passionate individuals to join our team!
We think you need these skills to ace Data Engineer in Newcastle upon Tyne
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience with data engineering, especially focusing on the technologies mentioned in the job description like Java, Kafka, and cloud platforms. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about data engineering and how you can contribute to our team. Share specific examples of your past projects that relate to the role, and don’t forget to show your enthusiasm for working with us at StudySmarter!
Showcase Your Projects:If you've worked on any relevant projects, whether personal or professional, make sure to include them in your application. We love seeing practical applications of your skills, especially if they involve building scalable data pipelines or using tools like Spark or Flink.
Apply Through Our Website:We encourage you to apply directly through our website for a smoother process. It helps us keep track of your application and ensures you get all the updates. Plus, it’s super easy to do!
How to prepare for a job interview at _nology
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, especially Java and tools like Kafka or Flink. Brush up on your programming skills and be ready to discuss your experience with ETL/ELT pipelines and real-time data processing.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of how you've tackled challenges in data engineering. Think about times when you optimised a data pipeline or resolved performance issues, and be ready to explain your thought process.
✨Understand Agile Methodologies
Since this role involves working in Agile teams, familiarise yourself with Agile principles and practices. Be prepared to discuss how you’ve collaborated with cross-functional teams and contributed to successful project outcomes.
✨Ask Insightful Questions
At the end of the interview, don’t hesitate to ask questions that show your interest in the company and the role. Inquire about their data architecture, the tools they use, or how they approach continuous improvement in their data systems.
