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- Tasks: Design and build scalable analytics architecture for a cutting-edge hospitality SaaS platform.
- Company: Exciting startup on the verge of closing Series A funding.
- Benefits: Competitive salary, equity options, and remote work flexibility.
- Why this job: Join a dynamic team and shape the future of data in the hospitality industry.
- Qualifications: Strong experience in data engineering and advanced SQL skills required.
- Other info: Fast-paced environment with opportunities for significant career growth.
The predicted salary is between 80000 - 90000 £ per year.
Are you a Senior Data Engineer who wants to join a scaling SaaS startup that is just about to close its Series A? A hospitality SaaS company are shaking up the industry, building a modern platform designed around real data.
Remote in UK or Europe. You will need to be +/- 2 hours UTC.
£80,000 - £90,000 + Equity
Interview Process: 3 stages – Interview with Founder, Interview with team members, Final Technical Interview.
Tech Stack: Angular, TypeScript, Liveview, Tailwind, Elixir, Node, Nest, Postgres, GraphQL, Git, Kubernetes, Websockets, AWS.
Today the platform already processes a large volume of transactional information and has a strong data foundation in place:
- ~500GB of structured transaction data
- 100M+ transactions across multiple restaurant types
- A data platform ready to scale
The next step is turning this into a high-performance analytics and data infrastructure that powers benchmarking, product insights and machine learning. That is where you come in.
We are looking for a Senior Data Engineer who can design and build the analytics architecture that sits alongside the transactional platform and unlocks the value of this data.
The Role: You will design and build the analytics infrastructure that powers data across the business. This includes building scalable pipelines, selecting and implementing the right warehouse architecture and ensuring analytical queries can run in seconds across hundreds of millions of records. You will work closely with engineers, data scientists and ML engineers to ensure the platform supports both product intelligence and machine learning use cases. This is a hands-on role where you will own the architecture, pipelines and performance of the analytics stack.
What You Will Be Doing:
- Design and build a scalable analytics architecture capable of handling hundreds of millions of transactions
- Build and optimise an OLAP analytics stack separate from the transactional database
- Design and maintain ETL / ELT pipelines moving data from transactional systems into the analytics warehouse
- Ensure data integrity across 100M+ transaction records
- Optimise analytical queries to deliver sub-second or few-second performance
- Implement monitoring, alerting and testing across data pipelines
- Support data modelling and suggest improvements to existing database structures
- Manage supporting infrastructure across the data platform
- Work closely with ML engineers to support model training and inference
- Collaborate with technical and non-technical stakeholders to turn business questions into efficient data models
- Contribute to the long term data architecture strategy
- Help establish data governance, consistency and quality standards across the platform
Experience We Are Looking For:
- Strong experience as a Senior Data Engineer
- Advanced SQL skills and strong query optimisation experience
- Experience building large scale analytics systems
- Hands-on experience with OLAP databases such as ClickHouse, Snowflake or Amazon Redshift
- Experience designing and implementing ETL pipelines from scratch
- Experience implementing caching strategies using Redis or AWS ElastiCache
- Strong knowledge of AWS infrastructure
- Ability to explain complex technical ideas to non-technical stakeholders
Nice To Have:
- Experience with real time analytics systems
- Experience building benchmarking or BI platforms
- Experience working with very large datasets
- Familiarity with orchestration tools such as Airflow, Kafka or AWS Glue
- Knowledge of partitioning and sorting strategies for large datasets
- Experience in startup or high growth environments
- Understanding of data science concepts such as clustering or recommendation
90 Day Outcomes: Within the first 90 days you will be expected to deliver clear progress on the analytics platform. This includes:
- Selecting the appropriate analytics warehouse based on performance, cost and scalability
- Designing and deploying ETL / ELT pipelines ingesting data from Amazon Aurora into the analytics warehouse
- Implementing monitoring and alerting across pipelines and warehouse infrastructure
- Reducing benchmark analytics query times to under 2 seconds
- Implementing a caching layer for frequently used analytical queries
- Evaluating the current data architecture and recommending improvements to support long term scale
If you’ve spent most of your career working for a large business, think about applying. You’ll need to be able to adapt, pivot and face the challenges that happen in startups.
Apply or DM for more information.
Senior Data Engineer employer: AG Talent
Contact Detail:
AG Talent Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior Data Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, especially those who work at startups. Use LinkedIn to connect and engage with them. You never know who might have a lead on that Senior Data Engineer role!
✨Tip Number 2
Prepare for those interviews! Research the company and its tech stack thoroughly. Be ready to discuss how your experience aligns with their needs, especially around building scalable analytics systems and optimising queries.
✨Tip Number 3
Show off your skills! If you have any projects or contributions to open-source that demonstrate your data engineering prowess, make sure to highlight them. Practical examples can really set you apart from the competition.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take the initiative to reach out directly.
We think you need these skills to ace Senior Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Senior Data Engineer role. Highlight your experience with analytics architecture, ETL pipelines, and any relevant tech stack you've worked with. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're excited about joining our team and how you can contribute to our mission. Be genuine and let your personality come through – we love that!
Showcase Your Projects: If you've worked on any relevant projects, make sure to mention them in your application. Whether it's building scalable analytics systems or optimising queries, we want to see your hands-on experience and problem-solving skills in action.
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 don’t miss out on any important updates. Plus, it shows you're keen to join our awesome team!
How to prepare for a job interview at AG Talent
✨Know Your Tech Stack
Familiarise yourself with the tech stack mentioned in the job description. Brush up on Angular, TypeScript, Elixir, and AWS, as well as OLAP databases like ClickHouse or Snowflake. Being able to discuss your experience with these technologies will show that you're ready to hit the ground running.
✨Prepare for Technical Questions
Expect technical questions during the final interview stage. Be ready to explain your approach to designing scalable analytics architectures and optimising ETL pipelines. Practise articulating complex concepts in a way that non-technical stakeholders can understand, as this is crucial for collaboration.
✨Showcase Your Problem-Solving Skills
Think of specific examples from your past work where you tackled challenges related to data integrity or query optimisation. Prepare to discuss how you approached these problems and the impact your solutions had on the business. This will demonstrate your hands-on experience and analytical mindset.
✨Ask Insightful Questions
During the interview, don’t hesitate to ask questions about the company's data architecture strategy or their plans for scaling the analytics platform. This shows your genuine interest in the role and helps you gauge if the company aligns with your career goals.