Data Engineer
Data Engineer at Xelix (London, hybrid) joining the Platform Team to own production data architecture for its AI-powered accounts payable platform: building Prefect pipelines, optimising PostgreSQL, and selecting storage/search tooling (OpenSearch, columnar warehouses) on AWS. Mid-to-senior hands-on role working alongside product, AI and DevOps engineers.
- Compensation
- £50K–£70K / yr
- Location
- London, GB
- Work setup
- Hybrid
- Seniority
- Middle
- Employment
- Full time
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- Professional experience owning the architecture of production data systems in AWS and their improvements
- Strong SQL, PostgreSQL and data modelling skills
- Solid Python and data pipeline engineering skills
- Experience with NoSQL databases (e.g. MongoDB, DocumentDB, DynamoDB)
- Experience with warehousing solutions (e.g. DuckDB, ClickHouse, Redshift)
- Experience with data access controls, retention and auditability requirements in regulated environments (SOC 1, SOC 2, HIPAA, ISO 27001 or similar)
- Good judgement on storage and processing trade-offs
- Clear communication and a hands-on, collaborative approach
- Focus on reliability, data quality and security
- Confidence using AI tools for development, investigation and documentation, with sound judgement about their value and limitations
Nice to have
- Experience improving pipeline orchestration with Prefect
- Experience with search and analytical technologies such as OpenSearch and column databases
Full description
About us
At Xelix, we work with some of the world’s largest companies to automate and strengthen their financial controls. Our AI solutions redefine how Accounts Payable teams operate – moving from manual processes to automated, intelligent workflows.
Xelix is a fast-paced scale-up – things move fast and expectations are high. We raised our Series B with Insight Partners in June 2025 and are expanding aggressively. We have a team of over 150 talented people pulling together to achieve our goals. Everyone is trusted to take ownership, move fast and have a meaningful impact. We prioritise personal and professional growth, keep things fun, and love to celebrate a milestone together.
In this role you’ll grow, be challenged and help shape the future of Xelix. If you’re excited about building something special with us, we’d love to hear from you.
About the role
We’re hiring a Data Engineer to join our growing Platform Team and help shape the data foundations behind our products. As our customer base and data volumes grow, you’ll make sure our production data architecture, pipelines and storage remain reliable, scalable and cost-effective.
This is a hands-on 1role focused on the systems that power our products, rather than internal BI or building a company-wide data warehouse. You’ll improve data models and database performance, help us choose the right storage and processing tools for each workload, and strengthen data quality and retention practices. Projects could include moving large and non-operational data out of PostgreSQL, improving pipeline orchestration with Prefect, and evolving our search and analytical capabilities with technologies such as OpenSearch and column databases.
You’ll bring the experience and judgement to assess our current architecture independently, constructively challenge established practices, identify the most valuable improvements, and take them from proposal through to production. Working closely with product engineers, AI engineers and DevOps, you’ll combine practical delivery with longer-term architectural thinking. Whether you’re an experienced mid-level engineer ready for broader ownership or already operating at senior level, this is an opportunity to take a dedicated data engineering role with a meaningful impact on how Xelix scales. At senior level, you’ll lead data architecture decisions across teams and own the roadmap for improving our production data systems.
What you'll be doing
- Improve our production data architecture.
- Build reliable pipelines with tools such as Prefect.
- Optimise PostgreSQL performance and data models.
- Choose the right storage, search and processing tools.
- Improve data quality and retention practices.