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For Companies At a Glance
- Tasks: Lead data engineering projects and collaborate on innovative AI solutions.
- Company: Fast-growing consultancy transforming enterprises with data and AI.
- Benefits: Competitive salary, bonuses, personal learning budget, and flexible gear options.
- Why this job: Join a diverse team and make a real impact from day one!
- Qualifications: Experience in data engineering, cloud platforms, and software development.
- Other info: Dynamic environment with excellent career growth opportunities.
The predicted salary is between 36000 - 60000 ÂŁ per year.
About Mesh-AI
We’re a transformation consultancy and we exist to reimagine how enterprises operate, making data and AI their competitive advantage. We turn enterprises into data-driven and AI enabled organisations, unleashing business growth and accelerating outcomes. We’re building an open, collaborative culture and we are always on the lookout for top talent to join us in our next phase of growth. If you’re interested in working on business-defining engagements with some of the brightest minds in the industry, apply below!
Opportunity
We recognise “data” is broad, that’s why we’re keen to speak to those from a heavy data background and cloud natives with experience in building software of high quality standards. As an outcome based consulting business we look for people who enjoy taking a consultative approach to solving problems in a holistic, end to end fashion. We have a highly talented, diverse team; from different backgrounds advisory, design blogs, technical forums.
Requirements
- Good grasp of modern data engineering (distributed systems, data streaming, event-based architectures etc) and how to apply them
- Experience with software engineering and DevOps best practices as well as the complete SDLC applied to data, e.g. automation, testing, contract definition, clean code, CI/CD, path to production
- Worked with AWS as a cloud platform
- Extensive hands-on experience with modern data technologies, ETL tools (e.g. Kafka, Flink, DBT etc.), data storage (e.g. Snowflake, Redshift, etc.) and also IaC (e.g. Terraform, CloudFormation)
- Software development experience with one or more languages (e.g. Python, Java, Scala, Go)
- Pragmatic approach to solving problems
Nice to have: keen interest in modern AI/ML techniques
Why Mesh-AI
- Fast-growing start-up organisation with huge opportunity for career growth
- Highly competitive salary package along with company bonus
- A hugely collaborative working environment where every person’s viewpoint is considered - a chance to make your mark on the business from day one!
- Financially backed business meaning security and support for new initiatives and global market expansion
- Pick your own Gear! Macbooks, PCs, Accessories!
- Drive your development with a personal learning budget
Want to know more? Get in touch with careers@mesh-ai.com. Otherwise apply here.
Lead Data Engineer employer: Mesh-AI
Contact Detail:
Mesh-AI Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Lead Data Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those at Mesh-AI. A friendly chat can open doors and give you insights that a job description just can't.
✨Tip Number 2
Show off your skills! If you've got a portfolio or projects that highlight your data engineering prowess, make sure to share them. It’s a great way to demonstrate your hands-on experience with modern data technologies.
✨Tip Number 3
Prepare for the interview by brushing up on your consultative problem-solving approach. Mesh-AI values holistic solutions, so be ready to discuss how you tackle challenges from all angles.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team.
We think you need these skills to ace Lead Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Lead Data Engineer role. Highlight your data engineering expertise, cloud experience, and any relevant projects you've worked on. We want to see how you can contribute to our mission!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about data and AI, and how your background makes you a perfect fit for Mesh-AI. Keep it engaging and personal – we love getting to know our applicants!
Showcase Your Projects: If you've worked on any cool data projects or have experience with modern data technologies, make sure to mention them! We’re keen to see your hands-on experience with tools like Kafka, Snowflake, or Terraform. It’s all about demonstrating your practical skills!
Apply Through Our Website: We encourage you to apply directly through our website for the best chance of being noticed. It helps us keep track of applications and ensures you’re in the loop about the process. Plus, it’s super easy – just a few clicks and you’re done!
How to prepare for a job interview at Mesh-AI
✨Know Your Data Engineering Stuff
Make sure you brush up on modern data engineering concepts like distributed systems and event-based architectures. Be ready to discuss how you've applied these in your previous roles, especially with tools like Kafka or Flink.
✨Show Off Your Software Skills
Since the role requires a solid grasp of software engineering and DevOps practices, prepare examples that showcase your experience with the complete SDLC. Talk about your automation efforts, testing strategies, and how you ensure clean code and CI/CD processes.
✨Familiarise Yourself with AWS
As AWS is a key platform for this position, make sure you can speak confidently about your hands-on experience with it. Highlight specific projects where you've leveraged AWS services to solve data challenges.
✨Be Ready to Problem-Solve
Mesh-AI values a consultative approach, so come prepared with examples of how you've tackled complex problems in a holistic way. Think about times when you had to consider multiple factors and stakeholders to arrive at a solution.