Founding Data Engineer

1. First data engineer at a funded London startup (two founders with proven track record)
2. Build data pipelines from scratch that process millions of mobile analytics events to predict user conversions weeks in advance
3. Direct business impact - your pipelines feed ML models that help subscription apps optimise £millions in ad spend

Day30 helps subscription apps improve paid acquisition ROI by providing predictive signals to optimise ad spend. We connect directly to mobile measurement partners (MMPs) to analyse behavioural event data, build ML models that predict high-value conversions weeks in advance, and deliver these predictions to advertising platforms without compromising user privacy.

We're a two-founder London startup combining deep expertise in performance marketing and machine learning. As our founding data engineer, you'll work directly with our CEO and CTO to build the data infrastructure that powers our platform. Your pipelines will process real-world messy data from millions of users, and your data quality instincts will be the difference between models that work and models that fail.

This role offers rare ownership: you'll architect our entire data processing infrastructure, establish data quality standards from day one, and have direct input on technical strategy. We're looking for someone who loves working with data - someone who gets excited about finding edge cases, building robust pipelines, and seeing their clean, validated datasets power accurate ML predictions.

• Build production ETL pipelines that process mobile analytics events from AppsFlyer, Adjust, and other MMPs
• Engineer features for ML models: temporal patterns, user behaviour sequences, campaign attribution
• Ensure data quality through comprehensive validation, deduplication, schema checks, and defensive programming
• Optimise pandas/Polars pipelines for performance - we process millions of events daily
• Handle messy real-world data: duplicates, out-of-order events, schema drift, missing fields, null handling

• Build Prefect workflows to orchestrate our multi-stage data pipeline
• Optimize and monitor BigQuery operations for cost and performance
• Establish CI/CD practices as we scale from a dozen clients to 100+ within 12 months

• Build API integrations with MMPs and advertising platforms (Python, REST APIs)
• Contribute to Django backend for client configuration and monitoring
• Collaborate with data scientists to productionise ML workflows and model inference

The ideal candidate must have…

• 4-6+ years building production data pipelines with demonstrable business impact
• Deep pandas expertise: you've built real ETL pipelines in production, understand vectorization, memory optimization, and performance patterns
• Deep expertise in Python and SQL with high code quality standards (e.g. ruff, mypy, uv, pytest, SQLFluff, pre-commit)
• Experience with messy real-world data: you've dealt with duplicates, nulls, schema drift, out-of-order events, and know how to build defensive pipelines
• Data quality mindset: you instinctively add validation checks, sanity-check implausible results, and think about edge cases before they become production bugs
• Right to Work in the UK (we cannot sponsor visas)
• Ability to work from Central London office 3 days/week (we believe in-person collaboration is crucial at this early stage)

You may be a great fit if you have experience with any of the following...

• Workflow orchestration tooling (e.g. Prefect, Airflow, Dagster)
• Experience with cloud data warehouses (e.g. BigQuery, Snowflake, Redshift)
• Data transformation tools (e.g. dbt) and data quality frameworks (e.g. Great Expectations)
• Backend Python frameworks (e.g. Django, FastAPI, Flask) for API development
• Modern data processing libraries (e.g. Polars, DuckDB)
• Infrastructure-as-Code (e.g. Terraform, Pulumi) and containerization (e.g. Docker)
• Open source contributions, or conference talks
• Ad-tech / mobile app analytics / subscription business experience

You don't need to be an expert in everything from day one. A good candidate will have deep expertise in at least one area with a desire to learn across the stack.

We respect your time and move quickly.

1. Application Review: We will look through your application (CV, screening questions, and code samples) to see if you meet the initial requirements for this role.
2. Initial Screen (20 mins with CTO): Short video call to assess mutual fit and technical background.
3. Practical Exercise (take home, up to 2 hours): We'll book you in for a 2-hour slot at any time. You will be given a real-world engineering challenge that mirrors our work at Day30. Use any tools you’d use on the job (including LLMs, Copilot, etc) - we care about approach and outcomes, not memorisation.
4. Technical Deep-Dive (60 mins, in-person): We will walk through your solution, discuss system design and architecture decisions, consider alternative approaches, and work through live problem solving.
5. Founder Conversation (30 mins): Meet both founders for us to understand your motivations and career goals, and for you to ask questions. We want to know that you'll be a great fit for our team, but we also want to help you achieve your goals too.

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• Base Salary: Up to £80,000 per annum (depending on experience)
• Holidays: 25 days of annual paid leave, plus bank holidays
• Equity: Meaningful options as first engineering hire
• Flexibility: 3 days/week in Central London office, remote otherwise
• Equipment: Top-spec MacBook Pro and any tools you need
• Learning Budget: Conferences, courses, and resources to stay current

Founder-Adjacent Position: Work directly with two second-time founders, participating in strategic decisions beyond just engineering. Your input will shape product direction and company culture.

Technical Ownership: Build core systems from near-scratch with full autonomy over technical choices. Balance pragmatism with excellence - we build robust solutions for real problems today while keeping tomorrow's needs in mind.

Immediate Impact: Your code ships to production quickly, directly affecting client performance and company revenue. No layers of bureaucracy or months-long deployment cycles.

Growth Trajectory: As we scale, you'll have the opportunity to shape engineering culture as our first engineering hire, defining best practices and building the team.