At a Glance
- Tasks: Manage data flow and ensure accuracy across operational systems and data warehouses.
- Company: Join Brainlabs, a dynamic agency shaping the future of media with a collaborative culture.
- Benefits: Enjoy a supportive environment, competitive salary, and opportunities for professional growth.
- Other info: Embrace a vibrant office culture with flexible working arrangements and a commitment to diversity.
- Why this job: Make a real impact by optimising data processes for top global brands.
- Qualifications: Experience in data management, strong Python/SQL skills, and ability to communicate technical concepts.
The predicted salary is between 51750 - 63250 £ per year.
Brainlabs is the independent, founder-led agency building the future of media. We have over 1000 Brainlabbers based across the globe, all united through the 12 principles laid out in our Culture Code. We're full-service, with people and AI agents working side by side, all aligned to one thing: maximizing our clients' revenue through a genuinely scientific approach to media. If you want to help play your part in achieving this for the world's biggest brands in an environment that will support and challenge you in equal measure, this is where you do it.
What's the role
We're looking for a Data Analyst to own the flow of data between our operational systems, our data warehouse and the rest of the business. Systems like Hubspot and our People Planner hold the client, revenue and pipeline data that Finance, Ops and Client Leadership rely on. This role keeps that data well structured, reliably synced and trustworthy as it moves into BigQuery and back out into the tools people use every day. A key part of the job is getting the right, live data presented cleanly in Notion, so client teams can actually use it to inform their day-to-day workflows.
What you do
- Build and maintain integrations that move operating system data into the data warehouse.
- Push data warehouse data back into operating systems to create complete workflows and data lifecycles.
- Debug data discrepancies across source systems and downstream tools, identifying the causes and automating solutions to prevent issues moving forward.
- Own HubSpot (or a comparable CRM) as a system of record: properties, associations, lifecycle stages etc. and the automation that keeps them consistent, including data quality, deduplication and governance at scale.
- Work closely with finance, operations, and other business support teams, translating technical decisions into terms non-technical stakeholders can act on.
Who you are
- Deep experience managing data structure (objects, properties, associations, lifecycle stages, automation), not just using it as an end user, with strong data quality and governance practices (deduplication, enrichment, validation, access controls, auditability).
- Able to turn a non-technical brief into a full plan and build it, and explain technical solutions to non-technical stakeholders without jargon.
- Comfortable in fast-moving rollouts, balancing speed with long-term architecture as requirements evolve.
- Experience building sync logic (canonical IDs, upserts, change detection, idempotent reruns) and ETL/ELT pipelines that survive repeated runs without duplicating or corrupting data.
- Experience integrating data into BigQuery, Snowflake or similar warehouses via API-based integrations, including reverse ETL back into business systems.
- Strong Python (or similar) and SQL for automation, data movement, transformation and validation across warehouse tables and views.
- Confident with REST APIs (auth, pagination, rate limits, retries, error handling) and GCP tooling (Cloud Run, Cloud Scheduler, Secret Manager, BigQuery, GitHub Actions) for building and orchestrating automated jobs.
- Able to debug data discrepancies across source systems and downstream tools, with good version control hygiene (GitHub or similar).
How you succeed
- Data moves between systems reliably and accurately at scale.
- Systems stay clean at scale, with properties and associations that hold up as volume grows.
- Data discrepancies get caught and fixed at the source.
- Finance, operations and other business teams trust the numbers.
- New integrations and automations are shipped with documentation and clear guidance.
- You balance shipping quickly against building for the long term, and can explain that trade-off to a non-technical stakeholder in plain terms.
What happens next?
We know searching for a job is tough and that you want to find the best career and employer for you. We also want to ensure that this position is the best fit for both you and us. Therefore, you will participate in a comprehensive interview process that includes skills interviews with our team. The goal of this process is to allow you to get to know us as we learn more about you.
A note on our offices: We pride ourselves in being a company with an office-centric culture and, for the majority of roles we expect people to be in commuting distance of the office and come in for around 3 days per week.
Brainlabs actively seeks and encourages applications from candidates with diverse backgrounds and identities. We are proud to be an equal opportunity workplace: we are committed to equal opportunity for all applicants and employees regardless of age, disability, sex, gender reassignment, sexual orientation, pregnancy and maternity, race, religion, or belief, and marriage and civil partnerships. If you have a disability or special need that requires accommodation during the application process, please let us know!
Please note that we will never ask you to transfer cash or make any other payment to us in order to apply for a role or to work for Brainlabs. Any such asks are fraudulent and should be reported to the appropriate authorities in your area.
Data Analyst, Operations employer: Brainlabs
At Brainlabs, we pride ourselves on being an exceptional employer in Greater London, offering a dynamic work culture that fosters innovation and collaboration. Our commitment to employee growth is evident through tailored development programmes and opportunities to lead transformative projects in AI and data services. Join us to be part of a forward-thinking team that values your expertise and encourages you to make a meaningful impact in the industry.
StudySmarter Expert Advice🤫
We think this is how you could land Data Analyst, Operations
✨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 Brainlabs!
✨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 Analyst, Operations at Brainlabs.
✨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 Brainlabs.
✨Apply Directly through Our Website
When you find a suitable opening like Data Analyst, Operations at Brainlabs, 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 Analyst, Operations
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 Brainlabs, 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 Brainlabs. 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 Brainlabs
✨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 Brainlabs!
✨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.
