At a Glance
- Tasks: Dive into data engineering and analytics, building impactful pipelines and models.
- Company: Join a small, ambitious data tech company where your work truly matters.
- Benefits: Enjoy hybrid working, real ownership, and hands-on experience with modern AI tools.
- Other info: Collaborative environment with opportunities for growth and learning.
- Why this job: Be part of a close-knit team shaping the future of data technology.
- Qualifications: Strong SQL skills and a passion for data; 2-4 years in data roles preferred.
The predicted salary is between 45000 - 55000 £ per year.
This is a dual-track role: roughly half your time will be spent on data engineering (pipelines, dbt models, CI/CD, data quality) and the other half on analytics and modelling (exploring data, quantifying value, supporting product and sales). You will be the second person on the data team, working closely with the Head of Data. This is not a narrow, ticket-driven role. We are a six-person company and we need people who roll their sleeves up. If something is critical to the business, we all work on it.
What You'll Work On
- Consumer identity & matching: building and proving out credit reference agency-style consumer identification and matching pipelines. This needs to work, and it needs to be demonstrably excellent.
- Bringing our quarterly model in-house: we currently rely on an externally owned statistical model that runs each quarter. You'll help us own and operate this ourselves.
- Data quality & monitoring: maturing how we observe and monitor data from source through to output. You'll help us catch issues before clients do.
- Product development: all of our products are data products. You'll work alongside the Head of Data to identify and build new features and data products.
- Sales support & proof of concepts: supporting the commercial team with POCs and POVs, with a view to automating the most repeatable ones.
- AI adoption: the data team leads AI usage across the business. You'll be hands-on with Claude and Gemini, finding ways to embed AI into our workflows and products.
Tech Stack
- Cloud - AWS
- Data warehouse - Snowflake
- Transformation - dbt (with CI/CD via GitHub Actions)
- Operational DB - MongoDB
- AI / LLM - Claude (Anthropic), Gemini (Google)
- Version control - GitHub
What We're Looking For
Must-haves
- Strong SQL: you think in SQL and write clean, well-structured queries as second nature
- Solid dbt experience: you understand models, tests, sources, and how to build maintainable pipelines, or are interested in learning quickly here.
- Consumer data background
- Data quality mindset: you have built or contributed to monitoring, alerting, or observability on data pipelines
- 2–4 years of hands-on experience in a data engineering or analytics engineering role
Strong advantages
- Experience with Snowflake
- Python for data modelling or ML; even if self-taught
- Familiarity with AWS (S3, IAM, basic infra)
- Any experience using LLMs or building AI-assisted tooling
- Supporting sales or non-technical stakeholders with data work
The person
- You're comfortable being the person who figures things out; we're small and there's no large team to hand off to
- You care about doing things properly: clean code, documented pipelines, data you'd stand behind
- You're genuinely curious about data and want to understand what it means, not just move it
- You're happy to work on whatever the business needs, even if it's outside your comfort zone
- You want to be part of something early and help shape how it grows
What We Offer
- Real ownership; you'll have meaningful responsibility from day one, not a queue of tickets
- A small, high-trust team where your work is visible and valued
- The opportunity to help build a data team and data function from close to the ground up
- Hands-on experience with modern AI tooling as a first-class part of the job
- Hybrid working with flexibility built in
About Us
We are a small, ambitious data technology company. Our entire business is data: we buy, enrich, and sell data on personal finance solutions, delivered to clients via API or data extract. We are a team of six, which means every person has real impact and genuine ownership of their domain. The data team sits at the core of the business: we own the Snowflake environment, build and maintain production data pipelines in dbt, and are responsible for the quality, integrity, and commercial value of everything we produce. Our Engineers handle ingestion (S3 → Snowflake) and write-back (Snowflake → S3 → MongoDB); the data team owns everything in between and increasingly, everything that drives the product forward. We are all in, together. A small team achieving great things means everyone pulls in the same direction: we help each other, share knowledge freely, and care about collective outcomes as much as our own. We move with purpose and take initiative. We don't wait for perfect conditions or perfect briefs: we get after problems, make progress, and adjust as we learn. We care about impact, not just activity. Our clients rely on the quality and integrity of what we produce, so we hold ourselves to a high standard. We take ownership. Every person here is Infinian: how we show up, the quality we ship, and the relationships we build with clients all reflect on the whole team.
A Note on the Title: We've listed this as Analytics Engineer because it best describes the blend of engineering and analytical work, but we're not precious about the label. If your background is in data engineering, data analysis, or a related field and you read this and thought 'that's me', please apply.
Analytics Engineer employer: Infinian Corporation
As a small, ambitious data technology company, we offer an exceptional work environment where every team member has real ownership and impact from day one. Our high-trust culture fosters collaboration and innovation, allowing you to grow alongside a dedicated team while working with cutting-edge AI tools in a flexible hybrid setting. Join us to shape the future of our data function and contribute to meaningful projects that drive our business forward.
StudySmarter Expert Advice🤫
We think this is how you could land Analytics Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills in real-time! Consider setting up a portfolio or GitHub repository showcasing your projects, especially those involving SQL, dbt, or data quality monitoring. This gives potential employers a taste of what you can do before they even meet you.
✨Tip Number 3
Prepare for interviews by brushing up on common technical questions related to data engineering and analytics. Practice explaining your thought process clearly, as communication is key when working in small teams like ours.
✨Tip Number 4
Don’t just apply anywhere; apply through our website! Tailor your application to highlight how your experience aligns with our needs. We love seeing candidates who understand our mission and are excited about being part of a small, impactful team.
We think you need these skills to ace Analytics Engineer
Some tips for your application 🫡
Show Your SQL Skills:Make sure to highlight your SQL expertise in your application. We want to see how you think in SQL and write clean, well-structured queries. If you've got any cool projects or examples, don't hesitate to share them!
Talk About Your Data Quality Mindset:We care about data quality, so let us know how you've contributed to monitoring or observability on data pipelines. Share specific experiences where you caught issues before they became problems for clients.
Be Curious and Open-Minded:We're looking for someone who's genuinely curious about data. In your application, express your eagerness to learn and tackle challenges outside your comfort zone. We love a can-do attitude!
Apply Through Our Website:Don't forget to apply through our website! It’s the best way for us to get your application and for you to stay updated on the process. We can't wait to hear from you!
How to prepare for a job interview at Infinian Corporation
✨Know Your Tech Stack
Familiarise yourself with the tech stack mentioned in the job description, especially SQL and dbt. Be ready to discuss your experience with these tools and how you've used them in past projects. If you're not familiar with something, show your curiosity and willingness to learn.
✨Showcase Your Problem-Solving Skills
This role requires a hands-on approach to problem-solving. Prepare examples from your previous work where you identified issues and implemented solutions, particularly in data quality or pipeline monitoring. Highlight your ability to roll up your sleeves and get involved.
✨Demonstrate Your Data Curiosity
Express your genuine interest in data and analytics during the interview. Share instances where you've gone beyond just moving data to understanding its implications. This will resonate well with the team’s culture of curiosity and ownership.
✨Be Ready for Collaboration
Since you'll be working closely with the Head of Data and supporting sales teams, prepare to discuss your experience collaborating with non-technical stakeholders. Emphasise your communication skills and how you’ve made complex data accessible to others.
