At a Glance
- Tasks: Own and evolve our Data Platform, designing scalable pipelines and driving architecture decisions.
- Company: Join a fast-evolving tech company committed to diversity and innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on mentorship and career development.
- Why this job: Make a real impact by collaborating with AI specialists on cutting-edge data solutions.
- Qualifications: 6+ years in Data Engineering with strong SQL, Python, and cloud experience.
The predicted salary is between 70000 - 90000 € per year.
The Team: Data Engineering
The Data Platform team powers trading insights, compliance reporting, and Business Intelligence across the organisation.
The Role
A senior individual contributor who will own and evolve our Data Platform end-to-end — designing scalable pipelines, championing modelling standards, and driving architecture and tooling decisions. You’ll partner closely with our AI/ML team to ensure high-quality, well-governed data fuels intelligent products.
What You’ll Do
- Collaborate with stakeholders to decompose ambiguous problems into tested data solutions.
- Develop dbt models, tests, and docs serving cross-functional stakeholders.
- Drive modelling decisions — dimensional models, snapshots, SCDs — across Exchange and Market Data.
- Design and maintain production data pipelines using Apache Airflow.
- Improve pipeline observability, alerting, and SLA monitoring; champion reliability practices.
- Mentor engineers via design reviews, pairing, and documentation.
- Evaluate new tools or patterns (streaming, real-time views) where they add value.
- Collaborate with the team’s AI specialists to deliver AI-driven Data Platform capabilities.
What You Bring
- 6+ years in Data Engineering, with 2+ years in a senior or lead capacity.
- Pragmatic problem-solver — you set direction, unblock others, and make trade-offs.
- Strong communicator — can whiteboard architecture and narrate data stories alike.
- Deep expertise in SQL, Python, and modern tooling (dbt, Airflow, BigQuery or equiv.).
- Strong data modelling fundamentals — you think in schemas, grain, and lineage first.
- Production GCP experience (BigQuery, GCS, GKE, IAM) or comparable cloud platforms.
- Solid grasp of Kubernetes concepts (pods, Helm charts, resource management).
Nice to Have
- Experience in crypto, digital assets, or exchange platforms — order books, market data.
- Familiarity with streaming frameworks (Kafka, Pub/Sub, Flink) and real-time patterns.
- Exposure to Data Governance or privacy frameworks in regulated finance.
- Experience building Conversational Analytics, RAG, or MCP-based AI tools.
Bullish is proud to be an equal opportunity employer. We are fast evolving and striving towards being a globally-diverse community. With integrity at our core, our success is driven by a talented team of individuals and the different perspectives they are encouraged to bring to work every day.
Senior / Lead Data Engineer in London employer: Bullish
At Bullish, we pride ourselves on fostering a dynamic and inclusive work environment where innovation thrives. As a Senior Data Engineer, you'll not only have the opportunity to lead impactful projects but also benefit from our commitment to employee growth through mentorship and collaboration with cutting-edge AI specialists. Located in a vibrant tech hub, we offer competitive benefits and a culture that values diverse perspectives, making us an exceptional employer for those seeking meaningful and rewarding careers.
StudySmarter Expert Advice🤫
We think this is how you could land Senior / Lead Data Engineer in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the data engineering field and let them know you're on the lookout for opportunities. A friendly chat can lead to referrals or insider info about job openings.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your best projects, especially those involving SQL, Python, and data pipelines. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on your problem-solving skills. Be ready to discuss how you've tackled complex data challenges in the past, and don't shy away from whiteboarding your thought process during technical interviews.
✨Tip Number 4
Apply through our website! We love seeing candidates who take the initiative. Plus, it gives you a chance to showcase your enthusiasm for joining our team and working on exciting data projects.
We think you need these skills to ace Senior / Lead Data Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Senior Data Engineer role. Highlight your expertise in SQL, Python, and any relevant tools like dbt and Airflow. We want to see how you can contribute to our Data Platform!
Craft a Compelling Cover Letter:Use your cover letter to tell us your story! Explain why you're passionate about data engineering and how your experience makes you a perfect fit for our team. Don’t forget to mention any experience with AI/ML collaboration, as it’s a big part of what we do.
Showcase Problem-Solving Skills:In your application, give examples of how you've tackled complex data challenges in the past. We love pragmatic problem-solvers who can break down ambiguous issues into clear solutions. Share your thought process and the impact of your work!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re keen on joining our awesome team at StudySmarter!
How to prepare for a job interview at Bullish
✨Know Your Data Engineering Fundamentals
Brush up on your data modelling fundamentals, especially around schemas, grain, and lineage. Be ready to discuss how you’ve applied these concepts in previous roles, as this will show your depth of knowledge and practical experience.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples where you've decomposed ambiguous problems into clear data solutions. Use the STAR method (Situation, Task, Action, Result) to structure your responses, highlighting your pragmatic approach to problem-solving.
✨Familiarise Yourself with Tools and Technologies
Make sure you’re well-versed in the tools mentioned in the job description, like dbt, Apache Airflow, and GCP. Be ready to discuss your experience with these technologies and how you’ve used them to design and maintain production data pipelines.
✨Communicate Effectively
Practice articulating your thoughts clearly, especially when discussing architecture and data stories. Being a strong communicator is key, so consider doing mock interviews or explaining complex concepts to a friend to build your confidence.
