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Data Operations Analyst
Application Deadline: 21 August 2026
Department: Engineering
Employment Type: Permanent - Full Time
Location: London
Description
We are a fast-growing Series A SaaS startup at the forefront of the next big shift in marketing, transforming the way brands connect with audiences in virtual worlds. This is a shift on par with the rise of social media, and we are building the analytics engine to power it. You will be joining a tight-knit, high-impact team of 40 people, including a product team of 3 and an engineering team of 10, meaning your work will directly shape the trajectory of our platform and company.
Our platform runs on data, and this role owns the work that keeps it flowing: pulling, validating, importing, and quality-checking so the wider team can move quickly and with confidence. Sitting within the product team, you will own the operational data layer: the day-to-day work of getting accurate, trustworthy data into the hands of product, client services, and the wider business, fast.
This is a hands-on product & operations role, not an engineering or a pure-analysis one. You are the dependable go-to who unblocks the team's data needs and keeps everything flowing and accurate. You write SQL confidently, lean heavily on AI tools to move quickly, and you are comfortable reading code and data models, but you take your pride from being the person who keeps data reliable and accessible, not from building pipelines or chasing the next engineering project.
Key Responsibilities
- Own data access across the product lifecycle: Be the team's first port of call for data requests, pull, shape, and deliver data in the format product, CS, and sales need.
- Validate and quality-check: Sanity-check, validate, and QC data across the platform so the team can trust every number. Spot anomalies, chase them down, and keep our data honest.
- Manage imports and ingestion ops: Run routine data imports and ingestion tasks, making sure data lands cleanly, completely, and on time.
- Work AI-first: Use AI tools (like Claude, Cursor, Copilot, and similar) to rapidly translate business logic or SQL into complex database queries (including NoSQL / Elastic / Mongo), accelerating repetitive work, and continually improve how the team gets and checks data.
- Document and share knowledge: Document data sources, queries, and processes so knowledge never sits with just one person. You make the team less dependent on any single individual, including yourself.
- Own the long tail: Take ownership of the steady stream of ad hoc requests that keeps the wider team moving.
- Partner cross-functionally: Work closely with data engineers and the product team, translating between technical data and real business needs.
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Skills, Knowledge, and Expertise
- An operations and service mindset: You take genuine pride in being the reliable go-to who keeps data flowing and accurate. You enjoy solving a team's everyday data needs and you are not looking to use this role as a stepping stone into a pure engineering job.
- Data Fluency (SQL & NoSQL): You write and debug SQL confidently, but you are also comfortable navigating non-relational/document databases (like MongoDB and Elasticsearch). You don't need to have raw NoSQL syntax memorized, but you should know how to read nested JSON structures.
- AI-first working: You already lean heavily on AI tools to work faster and better, and you are always finding new ways to use them.
- Technical literacy: You can read code and data models (including how relational data maps to NoSQL/JSON structures), understand how different systems fit together, and pick up light Python.
- Rigour and attention to detail: You are meticulous about data accuracy and quality, and you notice when a number looks off.
- Bias for action: You are comfortable in the ambiguity of a Series A startup and happy to roll up your sleeves and get things done.
- Clear communicator: You can translate between technical data and business stakeholders without friction.
- AI-first working: You already lean heavily on AI tools to work faster and better. Crucially, you possess the critical thinking to audit and validate AI-generated outputs, ensuring code/queries are safe and optimized before running them.
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Experience
- Around 2–4 years in a data operations, data analyst, BI, revenue/business operations, or similar hands-on data role.
- Experience in a data operations, analytics operations, or revenue operations function.
- Familiarity with the modern data stack and BI tooling.
- Background in marketing-related SaaS, virtual environments, or gaming.
- Familiarity with tools such as Linear and Notion.
Why join us?
- Join a business at the forefront of the next big shift in marketing.
- Be part of a fast-growing startup with a collaborative, innovative, and supportive team.
- Be genuinely indispensable, this role unlocks something the whole company depends on, so your impact is visible from day one.
- A real, non-engineering growth path: grow into owning our data-quality function, take on ROI and attribution research as the team scales.
- 25 days holiday as standard, plus a bonus GEEIQ Day to use whenever you choose.
- We offer Heka, a monthly wellness allowance you can spend across a wide range of fitness and wellbeing providers, plus a Cycle to Work scheme.
- We have a thriving company culture with regular socials, team offsites, and events - quizzes, sports days, Hackathons, Bake Offs, and more. Our eNPS is 52, nearly double the industry average, and it shows, the team genuinely loves working here and learning from each other.
- You pick your start time, we just ask that everyone's available during core hours of 10am–5pm. That might mean 8am–5pm, 9am–6pm, or 10am–7pm, whatever works best for you.
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