At a Glance
- Tasks: Design and optimise scalable data pipelines using Python, SQL, and Spark.
- Company: Join a growing tech startup focused on consumer data and analytics.
- Benefits: Hybrid working, competitive salary, and opportunities for professional growth.
- Other info: Exciting chance to work with AI-assisted tools and large datasets.
- Why this job: Make a real impact in a collaborative environment while exploring new technologies.
- Qualifications: Experience in data engineering with strong coding skills in Python and SQL.
The predicted salary is between 60000 - 75000 £ per year.
An exciting opportunity for a Data Engineer to join a growing technology business specialising in consumer data and analytics. Working within a small, talented team of engineers, analysts and data scientists, you'll play a hands-on role in developing and improving the data platform, building scalable data pipelines and working with large volumes of consumer data.
Strong coding fundamentals across Python, SQL and Spark are key, alongside a genuine interest in how AI is changing data engineering. You'll already be using AI-assisted development tools such as Claude in your day-to-day work, while having the technical knowledge to understand, review and challenge the code they produce.
This is an opportunity to join a collaborative startup environment where you can explore new technologies, contribute ideas and have a genuine impact as the business continues to scale.
Key Responsibilities
- Design, build and optimise scalable data pipelines and ETL/ELT processes.
- Develop and maintain data engineering solutions using Python, SQL and Spark.
- Work with large and complex datasets, ensuring data quality, reliability and performance.
- Use AI-assisted development tools to support coding, problem solving and delivery.
- Collaborate with engineering, analytics and data science teams to develop effective data solutions.
- Identify opportunities to automate processes and improve the scalability and efficiency of data workflows.
Essential Skills and Experience
- Strong hands-on data engineering experience.
- Strong coding fundamentals across Python and SQL.
- Hands-on experience working with Spark for large-scale data processing.
- Hands-on experience working with AWS data services.
- Experience designing and developing scalable data pipelines and ETL/ELT processes.
- Practical experience using AI-assisted development tools such as Claude within your day-to-day work.
- Strong communication skills and the ability to work effectively within a fast-moving, collaborative environment.
If you're a Data Engineer who understands the fundamentals but is also excited about how AI is changing the way engineers work, we want to hear from you. Apply today!
Please note we can only accept applications from those with current UK working rights for this role. This client cannot offer visa sponsorship.
Alternatively, you can refer a friend or colleague by taking part in our fantastic referral schemes! If you have a friend or colleague who would be interested in this role, please refer them to us. For each relevant candidate that you introduce to us (there is no limit) and we place, you will be entitled to our general gift/voucher scheme.
Datatech is one of the UK's leading recruitment agencies in the field of analytics and host of the critically acclaimed event, Women in Data.
AWS Data Engineer in London employer: Datatech Analytics
Datatech Analytics is an exceptional employer, offering a dynamic work culture that prioritises innovation and collaboration within the secure government and defence sectors. Employees benefit from meaningful projects that have a direct impact on national security, alongside opportunities for professional growth and development in cutting-edge technologies. With a hybrid working model and a focus on employee well-being, this role provides a unique chance to contribute to critical transformation programmes while enjoying a supportive and engaging workplace.
StudySmarter Expert Advice🤫
We think this is how you could land AWS Data Engineer in London
✨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 AWS Data Engineer 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 AWS Data Engineer 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 AWS Data Engineer in London
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.
