At a Glance
- Tasks: Build and maintain scalable data pipelines for innovative financial solutions.
- Company: Join a leading fintech company known for its entrepreneurial spirit and inclusive culture.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on inclusivity and career development.
- Why this job: Make a real impact on AI/ML projects that shape user experiences globally.
- Qualifications: Experience with data pipelines, Python, SQL, and modern programming environments.
The predicted salary is between 60000 - 80000 £ per year.
Aquent is partnering with a leading financial technology company dedicated to empowering individuals and businesses through innovative solutions.
Join our dynamic team as a Data Engineer and play a pivotal role in shaping the future of our international applications.
You will be at the forefront of building and maintaining robust data infrastructure, directly contributing to cutting‑edge AI/ML experiences and ensuring the seamless operation of our platforms at scale.
This is an opportunity to make a significant impact within an engineering‑focused organisation, gaining broad exposure to a comprehensive technology stack and contributing to projects that directly influence user experience and operational efficiency.
- What You’ll Do
- Build and maintain scalable data pipelines.
- Implement observability and monitoring solutions to drive reliability, data quality, and robustness.
- Develop critical data quality checks to ensure compliance and security standards are met.
- Actively participate in code and technical design reviews.
- Collaborate with product engineering, data science, and business teams on scoping and planning, supporting data tracking, collection, ingestion, quality monitoring, and alerting.
- What You’ll Bring
- Experience managing batch and/or streaming data pipelines and infrastructure.
- Proficiency in Python, SQL, and shell scripting.
- Experience with containerization and “infrastructure as code” tools.
- Professional experience developing applications in a modern programming environment (preferably Scala/Java and/or Python).
- Professional experience across the entire software development life cycle, from requirements gathering to production deployment using CI/CD.
- Ability to articulate complex technical content to peers and partners via Technical Design Documents.
- Nice‑to‑Have Qualifications
- Experience optimising complex analytical queries in a data warehousing system.
- Familiarity with data visualisation and business intelligence tools.
- Experience with the MLOps life cycle.
- Dev Ops experience with major cloud platforms.
- Data quality experience, particularly in developing and improving internal tools for automatic issue detection.
- Client Description
Our Client is a global technology platform that specialises in overcoming the world’s most important financial challenges.
Their products and services are driven by artificial intelligence, and their accounting software is one of their most recognisable creations.
Considered one of the top companies to work for, they are proud of their company culture and entrepreneurial spirit.
Aquent is dedicated to improving inclusivity & is proudly an equal opportunities employer.
We encourage applications from under‑represented groups & are committed to providing support to applicants with disabilities.
We aim to provide reasonable accommodation for any part of the employment process, to those with a medical condition, disability or neurodivergence.
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Data Engineer [211327] employer: Aquent
Aquent is an exceptional employer, offering a dynamic and collaborative work culture in the heart of London. With a focus on employee growth, you will have the opportunity to develop your strategic skills while working with prestigious clients in the financial services sector. The benefits package, including a competitive salary, performance bonuses, and private health insurance, combined with a hybrid work model, makes Aquent a rewarding place to advance your career.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer [211327]
✨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 Aquent!
✨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 Engineer [211327] at Aquent.
✨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 Aquent.
✨Apply Directly through Our Website
When you find a suitable opening like Data Engineer [211327] at Aquent, 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 [211327]
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 Aquent, 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 Aquent. 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 Aquent
✨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 Aquent!
✨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.
