At a Glance
- Tasks: Lead and mentor a dynamic Data Engineering team to deliver innovative AI solutions.
- Company: Fast-growing AI SaaS company with a focus on collaboration and growth.
- Benefits: Competitive salary, hybrid work model, and opportunities for professional development.
- Other info: Join a supportive environment where your ideas are valued and career growth is encouraged.
- Why this job: Shape the future of data engineering while working on cutting-edge AI products.
- Qualifications: Experience in leading Data Engineering teams and strong skills in Python and SQL.
The predicted salary is between 85000 - 95000 £ per year.
A fast-growing AI SaaS company is seeking an experienced Data Engineering Manager to lead and develop its Data Engineering team. This is an exciting opportunity to join at a pivotal stage of the company's growth. As Data Engineering Manager, you'll be responsible for leading the team, driving engineering standards and ensuring the successful delivery of scalable data solutions that underpin innovative AI products.
Working closely with Product, AI and Engineering teams, you'll help foster a product-led engineering culture, mentor and develop engineers, and provide the technical leadership needed to support the continued growth of the business. Whilst you'll retain the technical credibility to guide the team, this is primarily a leadership and management role rather than a hands-on development position.
What you'll be doing:
- Lead, mentor and develop a team of Data Engineers.
- Build and develop a high-performing engineering team.
- Drive a product-led engineering culture and embed engineering best practice.
- Define engineering standards, data models and ways of working.
- Oversee the design and delivery of scalable data platforms.
- Provide technical leadership across Python, SQL, ETL and ELT development.
- Drive CI/CD, automation and modern engineering practices.
- Collaborate with Product, AI and Engineering teams, building strong relationships with internal stakeholders and client technical teams to deliver innovative data solutions.
- Support recruitment, coaching and the continued growth of the Data Engineering function.
What you'll bring:
- Previous experience leading or managing Data Engineering teams.
- Strong leadership, mentoring and people development experience.
- A product mindset with the ability to balance technical excellence with business objectives.
- Strong stakeholder management and communication skills, with the ability to build effective relationships across Product, Engineering, AI and client technical teams.
- Strong experience with Python and SQL.
- Expertise building modern data platforms on Azure, AWS or GCP.
- Strong ETL and ELT engineering experience.
- Data modelling and data warehousing expertise.
- CI/CD, Git and engineering best practice.
Why join?
- Lead and shape a growing Data Engineering team within a fast-growing AI SaaS business.
- Build a product-led engineering culture focused on quality, collaboration and continuous improvement.
- Work on innovative AI and data products solving real business challenges.
- Collaborate with experienced engineers, architects and AI specialists in a supportive environment where ideas are valued and learning is encouraged.
- Play a key role in the continued growth and success of the Data Engineering function.
If you're looking for a role where you can influence technical direction, build great engineering practices and remain close to the technology, we want to hear from you.
Data Engineering Manager – London in Slough employer: Datatech Analytics
Join a fast-growing AI SaaS company in London as a Data Engineering Manager, where you'll lead and develop a high-performing team in a hybrid work environment. With a strong focus on fostering a product-led engineering culture, you'll have the opportunity to influence technical direction while collaborating with talented engineers and AI specialists. The company values continuous improvement and offers excellent growth opportunities, making it an ideal place for those seeking meaningful and rewarding employment.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineering Manager – London in Slough
✨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 Datatech Analytics!
✨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 Data Engineering Manager – London at Datatech Analytics.
✨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 Datatech Analytics.
✨Apply Directly through Our Website
When you find a suitable opening like Data Engineering Manager – London at Datatech Analytics, 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 Data Engineering Manager – London in Slough
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 Datatech Analytics, 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 Datatech Analytics. 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 Datatech Analytics
✨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 Datatech Analytics!
✨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.
