At a Glance
- Tasks: Lead data architecture and build scalable systems for high-volume data processing.
- Company: Wise, a global tech company revolutionising money management.
- Benefits: Competitive salary, inclusive culture, and opportunities for career growth.
- Other info: Join a diverse team dedicated to innovation and inclusivity.
- Why this job: Make a real impact on how millions manage their money globally.
- Qualifications: Strong Python skills and experience in Big Data and data architecture.
The predicted salary is between 59400 - 72600 £ per year.
Wise is a global technology company, building the best way to move and manage the world's money. As part of our team, you will be helping us create an entirely new network for the world's money.
We're looking for a seasoned, senior Data Engineer with a strong software engineering background to join our Scalable Growth team within the Global Product Tribe. You will serve as the technical authority on data architecture, bridging the gap between platform engineering and analytics engineering. You'll bring maturity, resilience, and scalability to our data infrastructure—building and shaping systems that handle high-volume streaming and batch data to fuel our global marketing engine.
If you thrive in an environment where you can scope complex problem spaces, mentor engineers in data best practices, and directly impact how Wise attracts millions of customers globally, this is the role for you.
Scalable Growth is dedicated to building enabling technology that helps Wise acquire customers at the lowest possible cost. Within this space, the Marketing Platform team owns the data pipelines, internal tools, and integrations with third-party vendors. As our dedicated data lead, you'll work cross-functionally with engineers, product managers, digital analytics leads, and marketing stakeholders to transform our marketing data infrastructure into a true platform product.
- Drive Data Architecture & Resilience: Audit, map out, and elevate our current data pipelines and streaming architectures. Establish best practices for monitoring, reliability, and scale across our data ecosystem (using Python, dbt, Airflow, Kafka, and Trino/Iceberg).
- Build the Unified Customer View: Productionize PoCs into scalable Airflow/dbt data workflows to lay the groundwork for our Customer Data Platform (CDP) datasets. Partner with analytics leads to define clear boundaries and standards for data ingestion, cleansing, and transformation—bringing a strong analytics engineering mindset (specifically via dbt) to raw data landing.
- Coach & Mentor: Elevate the data capabilities of software engineers in the squad through code reviews, architectural guidance, and hands-on mentoring.
- Python & Big Data Expertise: Advanced proficiency in Python and proven experience architecting, deploying, and maintaining Big Data and streaming/batch pipelines. Hands-on experience using dbt for scalable data transformations and Airflow for workflow orchestration.
- Data Architecture Mastery: A strong background in designing scalable, fault-tolerant data architectures, implementing data quality frameworks, and establishing production best practices. Ability to take a vague problem statement, independently uncover the requirements, scope project milestones, and drive solutions end-to-end. Familiarity with modern AI/ML data integration practices.
We're people building money without borders — without judgement or prejudice, too. Inclusive teams help us live our values and make sure every Wiser feels respected, empowered to contribute towards our mission and able to progress in their careers.
(Senior) Data Engineer (ML, Big Data) in London employer: Wise
Wise is an exceptional employer that fosters a culture of inclusivity and innovation, making it an ideal place for a Lead ML / Data Scientist to thrive. With a hybrid working model, generous personal development budgets, and a commitment to employee growth, you will have the flexibility and resources to excel in your role while contributing to meaningful projects that redefine how money moves globally.
StudySmarter Expert Advice🤫
We think this is how you could land (Senior) Data Engineer (ML, Big Data) 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 Wise!
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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 Wise.
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We think you need these skills to ace (Senior) Data Engineer (ML, Big Data) 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 Wise, 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 Wise. 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 Wise
✨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 Wise!
✨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.
