At a Glance
- Tasks: Build and maintain data pipelines for global clients using Python and SQL.
- Company: Join a dynamic global media agency with a collaborative culture.
- Benefits: Flexible work schedule, career progression, and supportive team environment.
- Other info: Opportunity to learn from experienced engineers and contribute your ideas.
- Why this job: Work on diverse projects and enhance your skills in a hands-on role.
- Qualifications: Strong Python, dbt, and SQL skills; cloud platform experience preferred.
The predicted salary is between 45000 - 55000 £ per year.
Looking for variety in your next data engineering role? Join a global media agency and work across a broad mix of clients and projects, with room to develop your skills and progress your career. You’ll be part of a collaborative, 14-person data engineering team supporting agencies across the network. With a strong team culture and experienced engineers to learn from, this is an opportunity to contribute your ideas and take ownership of meaningful technical work.
The role
You’ll build and maintain the pipelines that turn data from multiple sources into reliable, usable datasets for global clients. Your work will include:
- Building and extending data ingestion pipelines using Python, APIs and tools such as Adverity.
- Developing dbt models and SQL transformations tailored to each client’s requirements.
- Working with cloud infrastructure to deliver reliable, scalable data pipelines.
- Improving data quality through testing, troubleshooting and code reviews.
- Collaborating with analysts and reporting specialists to ensure datasets meet their needs.
This is a hands‑on data engineering role. BI dashboards and data visualisation are handled by a separate team.
About you
You’ll bring strong Python, dbt and SQL skills, alongside experience with a cloud platform. The team uses GCP, but experience with AWS or Azure is equally welcome. You’ll be comfortable writing, debugging and explaining your own code, including the reasoning behind your approach. AI tools can help enhance your work, but you’ll need the technical understanding to assess their output and take responsibility for what you build. Experience with API integrations, Git and data modelling will be valuable. Media or advertising data experience would be useful, but isn’t essential.
Why join?
You’ll have exposure to varied clients, different technical challenges and opportunities for progression within a global agency network. You’ll also join a supportive team that values curiosity, shared learning and well‑written code. If you’re looking for a role where you can stay hands‑on, broaden your experience and build your career.
Data Engineer - Media Agency in London employer: Silver Drum
Silver Drum provides a collaborative work culture that emphasizes professional development and innovation in analytics. Employees benefit from a hybrid working model and the chance to work on impactful projects with major clients. The company values data-driven decision-making and offers opportunities to enhance technical skills.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineer - Media Agency in London
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We think you need these skills to ace Data Engineer - Media Agency 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 Silver Drum, 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 Silver Drum. 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 Silver Drum
✨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 Silver Drum!
✨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.
