Zigzag Dog Training | B Corp
Analytics Engineer

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Analytics Engineer
Where Growth has no Leash Zigzag is the #1 app for dog training & life skills, helping pups and their pawrents build a lifelong bond. Too many dogs miss out on the right training, leading to behavioural issues or even abandonment. Our mission is to prevent that by making puppy training accessible and enjoyable for everyone.
We’ve been featured as Apple’s App of the Day, highlighted in New Apps We Love, and shortlisted for multiple industry awards. Backed by a major global petcare organisation (Purina), we’re a growing product-led company with big ambitions and a close-knit team.
What we are looking for We are looking for a curious, impact-driven Analytics Engineer to join our lean data team. You will sit at the intersection of data engineering and business analysis, transforming raw data into reliable, scalable insights.
In this role, you will be the "builder and investigator." You will take ownership of the "last mile" of our analytics stack—crafting dbt models, defining metrics, and partnering directly with Product and Marketing stakeholders to answer critical business questions. You will work closely with our Data Lead, who will guide your technical growth.
We are also a highly forward-thinking team when it comes to artificial intelligence. We use AI tools daily to accelerate our data operations, and the data team plays a key role in supporting the development and quality assurance of new AI features within our app. If you love writing clean SQL, embrace modern AI-assisted workflows, and want to see the direct business impact of your work in a fast-paced startup environment, this role is for you.
You will Build the Foundation: Develop, test, and maintain robust data models using dbt and BigQuery. You will transform messy, raw data into clean, business-ready datasets. Investigate & Solve: Lead ad-hoc data investigations. When business metrics shift or anomalies appear, you will be the detective writing the SQL and building the dashboards to explain why. Accelerate & Innovate: Actively use AI coding assistants and internal tools to speed up your development cycles, and collaborate with Product to monitor the quality and engagement of our app's AI features. Empower Stakeholders: Translate complex stakeholder requirements into self-serve dashboards and semantic metrics using Lightdash and Mixpanel. Data Quality: Implement dbt tests and maintain clear documentation to ensure our data remains trustworthy and accessible. Collaborate & Learn: Work in a "guided autonomy" model. You will execute independently on data modeling and reporting tasks while receiving mentorship on advanced architecture from the Data Lead.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
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Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
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Your profile Must-haves Experience: 2+ years of experience in a data-heavy role (Data Engineer, Data Analyst, BI Analyst, or Junior Analytics Engineer). SQL Mastery: You write clean, performant, and readable SQL (CTEs and window functions are second nature). dbt Experience: Hands-on experience building and testing models in dbt (Core or Cloud). You understand the value of version control (Git) and modular data modeling. Commercial Acumen: You care about the "So What?" You don't just build tables and dashboards; you want to understand how your data drives product engagement and marketing efficiency. AI-Forward Mindset: You are genuinely excited about the practical applications of AI. You aren't afraid to use LLMs to augment your coding speed, and you are eager to analyse how users interact with AI in our product. Communication: Strong ability to translate technical concepts to non-technical stakeholders and vice-versa. Nice-to-haves (but not dealbreakers) Experience with BigQuery. Experience with semantic layer BI tools (like Lightdash or Looker) or product analytics tools (like Mixpanel). Familiarity with Python (Pandas/scripting) for lightweight data manipulation. Previous experience in a high-growth startup environment.


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Why us? Compensation & Rewards Salary £45-55K pending on experience & interview performance, reviewed annually Annual 10% bonus, increasing with tenure Referral bonus
Wellbeing & Flexibility Private health scheme Hybrid working (UK-based) with 1-2 days per week in our London Victoria office Flexible working arrangements Work from abroad for up to 2 weeks per year 25 days holiday (increasing to 27), plus your birthday off Summer hours - early finish on Fridays during the months of July and August Dog-friendly office
Family and Lifestyle 4 weeks paternity leave 12 weeks maternity leave Summer party Christmas party
Growth & Learning LinkedIn Learning access £1,000 annual learning budget 4 giving back days per year 1 paid volunteering day per year
We’re happy to discuss flexible arrangements and reasonable adjustments throughout the hiring process.
About us Values, Inclusion & Diversity Equal opportunity runs through every aspect of Zigzag. We’re building a workplace where a diverse mix of people can do their best work and be their authentic selves. Our values guide how we work every day: User-led, data-informed: We test and measure to ensure we’re delivering real value Own your patch: Engineers have autonomy and responsibility over what they build Paw-sitive collaboration: Pairing, learning together, and shared ownership
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