At a Glance
- Tasks: Lead and scale a global data engineering team while driving innovative data strategies.
- Company: Established tech company undergoing exciting digital transformation.
- Benefits: Hybrid work model, competitive salary, and opportunities for professional growth.
- Other info: Join a dynamic environment with a focus on collaboration and innovation.
- Why this job: Shape the future of data engineering and influence technology strategy in a growing business.
- Qualifications: Proven leadership in data engineering with strong technical knowledge of modern architectures.
The predicted salary is between 75600 - 92400 £ per year.
Head of Data Engineering | Manchester | Hybrid (2-3 days)
We're looking for an experienced Head of Data Engineering to lead and scale a centralised data engineering function within an established, growing technology-led business.
The business is going through an exciting period of digital and data transformation, with new acquisitions, an expanding product portfolio and increasing investment in its data capabilities.
Data is becoming increasingly central to how the organisation operates, makes decisions and grows.
The Role
You'll take ownership of the data engineering function, working closely with senior leadership and teams across Data Science, Analytics, Product and Engineering.
Key responsibilities will include
- Leading, developing and mentoring a global team of Data Engineers, including managers.
- Defining and delivering the wider data engineering strategy, aligned with business objectives.
- Owning the data architecture, ensuring platforms and pipelines are scalable, robust and fit for the future.
- Driving best practice across data ingestion, transformation, storage and integration.
- Providing technical direction around modern data platforms, particularly Databricks and medallion architecture.
- Championing data quality, governance, observability and security.
- Working closely with senior stakeholders to identify opportunities, resolve challenges and remove blockers.
- Evaluating new technologies and approaches to continually improve the data engineering capability.
What We're Looking For
- Proven experience leading a Data Engineering function or large engineering team.
- Strong technical knowledge of modern data architecture, particularly medallion architecture and Databricks.
- Experience designing and delivering scalable data platforms and pipelines.
- Strong programming experience with Python, Scala or Java.
- Exposure to ETL tooling such as Rivery and MLOps frameworks.
- Experience working with cloud platforms across AWS, Azure or GCP.
- The ability to operate strategically while retaining enough technical depth to influence solution design.
- Experience managing managers and supporting geographically distributed engineering teams.
- Excellent stakeholder management and communication skills, with the ability to translate complex technical concepts into clear business outcomes.
Interested?
If you're an experienced Data Engineering leader looking for an opportunity to shape the direction of a growing data function, build great teams and influence technology strategy, I'd be keen to hear from you.
Head of Data Engineering in Manchester employer: MRJ Recruitment
As a leading player in the logistics sector, we pride ourselves on fostering a dynamic and inclusive work culture that empowers our employees to thrive. Our commitment to professional development ensures that you will have ample opportunities for growth while working on innovative projects like our core systems modernisation programme. Located in a vibrant area, we offer a collaborative environment where your contributions directly impact our journey towards a future-ready operating model.
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We think you need these skills to ace Head of Data Engineering in Manchester
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!
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