At a Glance
- Tasks: Design and maintain scalable data pipelines using SQL and Python.
- Company: Global data and AI consultancy transforming data into business results.
- Benefits: Hybrid working, flexible hours, and varied client-facing projects.
- Other info: Opportunity to mentor others and work with cutting-edge technologies.
- Why this job: Join a dynamic team and lead impactful data engineering projects.
- Qualifications: 3-7 years in data engineering with strong SQL and Python skills.
The predicted salary is between 60750 - 74250 £ per year.
A global data and AI consultancy helping leading organisations turn data into real business results. You’ll join a growing London team of data engineers, data scientists and consultants, working on varied, client-facing projects.
The role
- Design, build and maintain scalable data pipelines using SQL and Python
- Lead the integration of complex data systems, keeping data consistent and accurate across platforms
- Work with architects, analysts and stakeholders to turn business requirements into technical delivery
- Mentor and manage a team of data engineers
- Set and enforce best practice on data governance, security and compliance
What you’ll need
- 3 to 7 years in data engineering, with real ownership of production pipelines
- Strong SQL and Python, plus hands-on Git
- Production experience with at least one of Databricks, Snowflake, Azure Data Factory, AWS Glue, Apache Airflow or PySpark
- Cloud-native orchestration and distributed data processing
- Infrastructure as code (e.g. Terraform)
- CI/CD and DevOps or DevSecOps practice
- A sound grasp of data architecture: data mesh, lake, warehouse and lakehouse
- Clear communication, with the ability to explain technical decisions to non-technical stakeholders
Nice to have
- Azure, AWS or GCP certifications
- Databricks, Snowflake or similar certifications
What’s on offer
- Hybrid working with a flexible approach to remote days
- Managed working hours, 9am to 6pm
- Varied, client-facing projects across a range of industries
Please note: applicants must already have the right to work in the UK.
Data Engineer employer: dcoded. | B Corp ™
dcoded. | B Corp ™ provides a collaborative work culture where employees can engage in impactful projects and develop their skills. The company values mentorship and offers a hybrid working model, allowing for a balanced work-life integration.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer
✨Get Involved in Data Science Meetups
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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 Engineer at dcoded. | B Corp ™.
✨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 dcoded. | B Corp ™.
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When you find a suitable opening like Data Engineer at dcoded. | B Corp ™, 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 Engineer
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 dcoded. | B Corp ™, 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 dcoded. | B Corp ™. 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 dcoded. | B Corp ™
✨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 dcoded. | B Corp ™!
✨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.
