Why Ballpoint
Ballpoint is a fast-scaling, strategy-led creative and marketing science agency built for consumer brands who want to grow fast and grow properly. We combine creative, data, and experimentation to help cool brands kill their competition.
As a team, we’re made up of people at the top of their game, all with exceptional experience and a strong track record.
Ballpoint started because we knew first-hand what ambitious brands actually need from an agency: not big promises and account handlers, but operators who treat their business like it’s their own. We’ve been on the receiving end of all the problems. So we became the solution. Today, every Ballpoint strategist is ex-in-house senior talent. The people running client growth here are the people who used to hire agencies, not career account handlers learning on your budget.
Our clients reflect who we are: early innovators, fast-growing startups, and household names pushing into their next phase of growth. Each brings a new challenge for us to break through creatively, strategically, and through marketing science.
Why you
People who thrive at Ballpoint are self-motivated and take genuine ownership of their work. They’re commercially minded and confident making decisions, with a knack for solving problems quickly without losing clarity.
Whatever their discipline, they care deeply about their craft and are naturally curious. We want people who are always learning, always sharing, and always raising the bar. They’re comfortable challenging others and being challenged themselves, and they know how to inspire confidence with clients and teammates alike. If that sounds like you, you’ll fit right in!
We’re scaling quickly and we want the best people with us on the journey.
About this role
You will be the architect of our internal data engine. You aren't just running reports; you are building the infrastructure that makes reporting possible. You will move us away from fragile spreadsheets and manual exports to a robust, code-first Modern Data Stack.
You’ll build the infrastructure behind our clients’ growth programmes, covering attribution, forecasting, and experimentation, while ensuring our data pipelines are automated, tested, and scalable.
We currently have a strong fractional setup in place, but we’re building towards a full-time in-house function.
You’ll inherit solid foundations and a smooth handover, then take ownership of the full stack as we scale.
Our Data Stack
We believe in using the best tools for the job. You will be taking ownership of a stack built on:
- Ingestion: Weld
- Warehousing & ML: BigQuery and Vertex AI
- Transformation: dbt
- Orchestration: Airflow
- BI: Looker Studio
What This Role Involves
This is a Full Stack Analytics Engineering role. You will own the lifecycle of data from the API source to the final dashboard.
1. Data Integration & Orchestration (ELT)
- Pipeline Architecture: You will own the ingestion layer, using Weld to pull data from Meta, Google Ads, Shopify, GA4, and other sources into BigQuery.
- Orchestration: You will manage the scheduling, dependencies, and monitoring of our data workflows using Airflow, ensuring data arrives on time and in the correct sequence.
- Custom Ingestion: You will write custom Python scripts/extractors to fetch data where out-of-the-box connectors fail or are not available.
2. Transformation, Quality & Observability (dbt)
- The Semantic Layer: You will build and maintain our dbt project. You will write modular, version-controlled SQL and document it in YAML files.
- Data Quality & Testing: You will treat data like software. You will implement robust dbt tests (schema tests, custom data tests) to catch nulls, duplicates, and anomalies before they reach the client.
- Observability: You will own the "health" of the data. You will set up alerts for freshness and quality failures, ensuring we know about a broken pipeline before the client does.
3. Advanced Analytics & Marketing Science
- Statistical Modelling: You will use Vertex AI and R/Python to go beyond basic aggregation, supporting the team with Marketing Mix Modelling (MMM), incrementality testing, and customer lifetime value (LTV) forecasting.
- Business Intelligence: You will connect your clean dbt models to our BI tools (Looker Studio, Tableau, or Lightdash) to create self-service dashboards that allow the strategy team to answer their own questions.
4. Collaboration & Enablement
- Handover: Work directly with our fractional data analyst during the handover period to absorb context.
- Empowerment: Arm the wider team with the tools, clarity, and insight they need to make the best decisions.
- Approach: Bring a calm, methodical, commercially minded approach to the data.
Skills & Experience
You should have:
- 3–5+ years experience in an Analytics Engineering, Data Engineering, or technical Data Analysis role.
- dbt Mastery: Deep experience building dbt projects from scratch. You know how to use Jinja macros, manage dbt run cycles, and structure a project for scale.
- Python & R: Strong proficiency in Python (for data pipelines/automation) and R (for statistical analysis/marketing science).
- Orchestration & Integration: Experience managing Weld and orchestrating workflows with Airflow.
- GCP Ecosystem: Hands-on experience with BigQuery and Vertex AI.
- Data Quality Focus: A strong track record of implementing automated testing (dbt tests) and observability. You don't trust data until you've tested it.
- Git/Version Control: You are comfortable with the command line, branching strategies, and pull requests.
- Commercial Context: Practical familiarity with paid media (Meta/Google), Shopify/DTC/SaaS models, and attribution.
Desirable:
- Experience with Looker (or similar) as the visualisation layer.
- Experience building Marketing Mix Models (MMM).
- Knowledge of CI/CD pipelines for data (GitHub Actions/GitLab CI).
What does success look like
30 Days: Audit, Stabilise & Quick Wins
- The Handover: You have fully absorbed the context from our fractional analyst and taken ownership of all admin/permissions (GCP, Weld, GitHub).
- The Audit: You have mapped our data lineage and identified exactly where business logic is hiding (e.g., buried in Looker Studio calculated fields) versus where it should be (dbt).
- The Fix: You’ve set up dbt tests on our most critical tables. We are no longer discovering data breaks because a client emailed us; you are catching them first.
- The Win: You have shipped one high-impact fix or automation that saves the Strategy team 5+ hours of manual work.
60 Days: Migration & Standardisation
- Logic Migration: You have aggressively refactored our legacy reporting. You’ve moved complex logic out of Looker Studio/Spreadsheets and into version-controlled dbt models.
- Orchestration: Airflow is humming. You have optimised our DAGs so data arrives before the team logs on, not during their morning coffee.
- Custom Ingestion: You have deployed a custom Python extractor for a data source that the out-of-the-box Weld connector couldn't handle (e.g., a niche influencer platform or TikTok API).
- Trust: The Strategy team has stopped "double-checking" the dashboard numbers against the ad platforms.
90 Days: Value, Science & Scale
- Advanced Modeling: You are leveraging Vertex AI or R/Python to deploy our first "Marketing Science" initiative, whether that’s a basic MMM (Media Mix Model) or a contribution margin forecast.
- Speed to Insight: You have templatised our onboarding. When a new client joins Ballpoint, we can spin up their data stack and standard dashboards in hours, not days.
- Proactive Partner: You aren't just answering tickets. You are joining client strategy calls, spotting trends in the data that the strategists missed, and suggesting where we should test next.
Package
- Competitive salary
- 28 days paid holiday excluding bank holidays
- £1,500 tech budget
- EMI Scheme
- Enhanced Parental Leave Policy
- Work from anywhere days
- Monthly socials
- Regular team lunches
- Annual offsite
