At a Glance
- Tasks: Design and maintain high-volume data pipelines for AI-driven advertising solutions.
- Company: Join a leading AdTech company revolutionising digital publishing with AI.
- Benefits: Competitive salary, equity, fully remote work, and budget for professional development.
- Other info: Enjoy autonomy in a collaborative, evolving environment with a distributed team.
- Why this job: Be at the forefront of AdTech innovation and make a real impact.
- Qualifications: Strong data engineering skills, experience with streaming systems, and AWS knowledge.
The predicted salary is between 48000 - 72000 £ per year.
PubX builds next-generation publisher-first agentic advertising infrastructure. Our AI makes real-time, revenue-critical pricing decisions for digital publishers. Our Bid Intelligence uses machine learning to optimize every programmatic ad auction individually, generating measurable revenue uplift for publishers. We’re currently ranked #5 globally in Prebid Analytics Adapter Rankings, and growing.
The problem we’re solving: Digital publishers leave significant revenue on the table because ad pricing is still largely manual, static or simple rule-based. Every ad impression is unique, but most pricing systems treat them the same. PubX's AI analyzes bid-stream data and historical patterns to arrange optimal deals, in near real-time.
As a founding member of AgenticAdvertising.org, we’re building the next generation of autonomous advertising infrastructure.
What You’ll Work On
- Tech: Python, SQL, Spark, Airflow, dbt, Kafka/Kinesis/SQS, AWS, Terraform/CDK, modern ETL
- Design and maintain high-volume data pipelines (batch + streaming) powering agentic AI features and core product workflows
- Build event-driven components using Kafka and message queues, including idempotency patterns, replay strategies, and backfill mechanisms
- Develop data models and transformation layers (lakehouse patterns, dbt-style modeling) supporting both analytics and ML/AI consumption
- Own data quality and reliability: schema management, validation, lineage, SLAs, and incident response
- Enable AI/ML workflows with robust datasets for training, evaluation, feature generation, and feedback loops from production agents
- Deploy and operate data infrastructure on AWS using infrastructure-as-code
What We’re Looking For
- We’re looking for an experienced engineer who has worked on production systems and enjoys solving practical problems with AI.
- You’ve likely have: Strong data engineering fundamentals: data modeling, partitioning, performance tuning, and cost-aware design for high-volume workloads
- Experience building streaming and event-driven systems (Kafka/queues), including handling late/out-of-order events, backfills, and real-world data edge cases
- Strong SQL + Python skills, and comfort with modern data stack tooling (e.g., Spark, Airflow/Dagster, dbt, warehouse/lakehouse patterns)
- Hands-on AWS experience with production operations for data systems: monitoring, incident response, and security considerations (PII, access control, encryption, auditability)
- Familiarity integrating data with AI/ML and agentic systems: feature pipelines, evaluation datasets, grounding/citations inputs, and feedback capture from agent outcomes
You tend to:
- Make pragmatic decisions balancing speed, quality, cost, and risk trade-offs.
- Communicate technical ideas well in writing and conversation to both technical and non-technical audiences.
- Write clean, well-tested code with thoughtful abstractions that’s easy to extend and operate.
- Learn quickly when things are unfamiliar by prototyping, then hardening and documenting what you ship.
Bonus (not required):
- Experience with AdTech or other high volume real-time systems
Who This Role Will Suit
This role suits engineers who like a mix of autonomy and collaboration, and who are comfortable working in an environment that’s still evolving. We’re a distributed team with a growing engineering presence in India, so comfort with async collaboration and clear written communication is important. We use agentic coding tools heavily (e.g. Cursor and Claude Code) to plan, scaffold, refactor, and debug production code, while maintaining strong engineering judgment and ownership of outcomes.
Company Benefits
- Competitive salary with meaningful equity
- Fully remote, async-friendly working
- Budget for learning and professional development
Interview Process
Our process is designed to be practical and respectful.
- CV & Profile Review – Relevant experience and background
- Initial Chat (30 mins) – Motivation and role fit
- Technical Interview (60 mins) – Architecture, design choices, and real scenarios
- Practical Exercise + Discussion (60 mins) – A small task related to the role
If you’re interested in building and shaping real systems in a growing product company, at the forefront of AdTech innovation, we’d love to hear from you.
We will process your personal data in accordance with our Recruitment Privacy Notice: https://pubx.ai/privacy/recruitment/
Senior Data Engineer (Agentic AI) employer: pubX
At pubX, we pride ourselves on being an exceptional employer in the heart of Greater London, offering a vibrant work culture that fosters innovation and collaboration. Our commitment to employee growth is evident through continuous learning opportunities and a supportive environment where autonomy is encouraged. Join us to be part of a forward-thinking team that is shaping the future of AdTech, while enjoying the unique advantages of working in one of the world's most dynamic cities.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Engineer (Agentic AI)
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those already at PubX or similar companies. A friendly chat can open doors and give you insider info on what they’re really looking for.
✨Tip Number 2
Show off your skills! If you’ve got a portfolio of projects or contributions to open-source, make sure to highlight them. Real-world examples of your work with Python, SQL, or data pipelines can set you apart from the crowd.
✨Tip Number 3
Prepare for the technical interview by brushing up on your data engineering fundamentals. Be ready to discuss your experience with streaming systems and AWS operations, as these are key to the role at PubX.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining the team at PubX.
We think you need these skills to ace Senior Data Engineer (Agentic AI)
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the job description. Highlight your data engineering fundamentals, especially in Python and SQL, as well as any experience with streaming systems like Kafka.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're excited about the role and how your background aligns with our mission at PubX. Share specific examples of your past work that demonstrate your problem-solving skills in AI and data engineering.
Showcase Your Projects:If you've worked on relevant projects, whether personal or professional, make sure to include them. We love seeing practical applications of your skills, especially those involving high-volume data pipelines or event-driven systems.
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 team!
How to prepare for a job interview at pubX
✨Know Your Tech Stack
Familiarise yourself with the technologies mentioned in the job description, like Python, SQL, and Kafka. Be ready to discuss your experience with these tools and how you've used them in past projects. This shows you’re not just a fit on paper but can also bring real-world experience to the table.
✨Prepare for Technical Questions
Expect questions about data engineering fundamentals and event-driven systems. Brush up on concepts like data modelling, performance tuning, and handling late events in streaming systems. Practising coding problems related to these topics can help you articulate your thought process during the interview.
✨Showcase Your Problem-Solving Skills
Be prepared to discuss specific challenges you've faced in previous roles, especially those involving AI and data pipelines. Use the STAR method (Situation, Task, Action, Result) to structure your answers, highlighting how you approached problems and what impact your solutions had.
✨Communicate Clearly and Confidently
Since the role involves working with both technical and non-technical teams, practice explaining complex concepts in simple terms. During the interview, make sure to listen actively and ask clarifying questions if needed. This demonstrates your collaborative spirit and ensures everyone is on the same page.
