Data Engineer

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, * 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

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• 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

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