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For Companies At a Glance
- Tasks: Lead a team to build scalable, real-time data infrastructure and drive technical vision.
- Company: Join a high-growth digital business undergoing a significant data transformation.
- Benefits: Enjoy hybrid working, competitive salary, private healthcare, and regular team socials.
- Why this job: Shape strategy in a fast-paced environment while mentoring engineering talent.
- Qualifications: Strong experience in leading data teams and expertise in real-time processing tools required.
- Other info: Full ownership of team growth and platform direction awaits you!
The predicted salary is between 54000 - 84000 £ per year.
We’re supporting a high-growth digital business as they build out their internal engineering function and undergo a significant data transformation. As part of this next phase, they’re hiring a Data Engineering Manager to lead a growing team of engineers focused on building scalable, real-time data infrastructure from the ground up.
About the Role
This is a strategic, hands-on leadership role where you’ll drive the technical vision, define best practices, and lead a team responsible for architecting, building, and maintaining a real-time data platform. You’ll collaborate cross-functionally with product, analytics, and engineering teams to deliver robust, high-performance pipelines and infrastructure that power data-driven decision-making across the business.
Key Responsibilities
- Lead, mentor and grow a team of data engineers
- Set the technical direction and oversee the delivery of real-time data solutions
- Design scalable and fault-tolerant data infrastructure in the cloud
- Drive adoption of best practices around data quality, governance, and pipeline monitoring
- Collaborate with stakeholders to align data engineering efforts with business goals
- Stay on top of new technologies and promote continuous improvement across the team
What You’ll Need
- Strong experience leading data engineering teams in high-growth environments
- Deep expertise with real-time data processing tools (e.g. Kafka, Flink, Spark Streaming)
- Solid hands-on knowledge of cloud platforms (AWS, GCP or Azure)
- Strong proficiency in languages like Python, Java or Scala
- Familiarity with orchestration tools such as Airflow or Kubernetes
- Strong stakeholder management and communication skills
- Passion for mentoring and developing engineering talent
What’s On Offer
- Competitive salary + quarterly bonus scheme
- Hybrid working with a central London HQ
- Private healthcare and market-leading benefits
- Regular team lunches, socials, and annual international offsite
- Fast-paced, innovative environment with clear scope to shape strategy
- Full ownership of team growth and platform direction
Data Engineering Manager employer: Atarus
Contact Detail:
Atarus Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineering Manager
✨Tip Number 1
Familiarise yourself with the latest real-time data processing tools like Kafka, Flink, and Spark Streaming. Being able to discuss these technologies confidently during your interview will show that you’re not only knowledgeable but also passionate about the field.
✨Tip Number 2
Highlight your leadership experience by preparing examples of how you've successfully mentored and grown teams in previous roles. This will demonstrate your capability to lead a team effectively, which is crucial for the Data Engineering Manager position.
✨Tip Number 3
Research the company’s current data infrastructure and any recent projects they’ve undertaken. This knowledge will allow you to tailor your discussions around how you can contribute to their specific goals and challenges.
✨Tip Number 4
Prepare to discuss your experience with cloud platforms like AWS, GCP, or Azure. Be ready to share specific examples of how you've designed scalable and fault-tolerant data solutions in the cloud, as this is a key requirement for the role.
We think you need these skills to ace Data Engineering Manager
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in leading data engineering teams and your expertise with real-time data processing tools. Use specific examples that demonstrate your hands-on knowledge of cloud platforms and programming languages relevant to the role.
Craft a Compelling Cover Letter: In your cover letter, express your passion for mentoring and developing engineering talent. Discuss how your leadership style aligns with the company's goals and how you can contribute to their data transformation journey.
Showcase Relevant Projects: Include details about past projects where you designed scalable data infrastructure or implemented best practices around data quality and governance. This will help illustrate your capability to drive technical vision and deliver real-time data solutions.
Highlight Stakeholder Management Skills: Emphasise your strong communication skills and experience in collaborating with cross-functional teams. Provide examples of how you've successfully aligned data engineering efforts with business objectives in previous roles.
How to prepare for a job interview at Atarus
✨Showcase Your Leadership Skills
As a Data Engineering Manager, you'll need to demonstrate your ability to lead and mentor a team. Prepare examples of how you've successfully guided teams in the past, focusing on your leadership style and how you foster growth and collaboration.
✨Highlight Technical Expertise
Be ready to discuss your hands-on experience with real-time data processing tools like Kafka, Flink, or Spark Streaming. Make sure to provide specific examples of projects where you've implemented these technologies and the impact they had on the business.
✨Discuss Cloud Platform Knowledge
Since the role requires solid knowledge of cloud platforms, be prepared to talk about your experience with AWS, GCP, or Azure. Share insights on how you've designed scalable and fault-tolerant data infrastructure in the cloud.
✨Emphasise Stakeholder Collaboration
Collaboration is key in this role, so think of instances where you've worked cross-functionally with product, analytics, or engineering teams. Highlight how you aligned data engineering efforts with business goals and the outcomes of those collaborations.
