Data Engineer (AI Startup)

• Matt, Cofounder and CTO, is the hiring manager
• We work Mon-Thu in our office in Chancery Lane, London
  
  

The basics

• Your title will be Data Engineer
• This role pays £70k-£110k/year + equity
• Matt, Cofounder and CTO, is the hiring manager
• We work Mon-Thu in our office in Chancery Lane, London
  
  

What are we building? An AI Executive Assistant

In 1930, the economist John Maynard Keynes predicted that we'd only be working 15 hours a week by 2030. Despite automation in agriculture and industry, that hasn't happened.

Why? The service sector. Walk around the average office and you'll see people's days taken up by emails, Slack and meetings instead of real work.

People in client facing roles - sales, professional services firms, recruiters - feel this most acutely. Instead of advising clients, they spend hours on admin. Following up. Scheduling meetings and taking notes on them. Answering repetitive questions over email.

We've built an AI executive assistant that looks at your emails, messages and meetings, and uses that knowledge to answer your email, schedule meetings, take next steps from meetings and organise your inbox.

Unlike other startups, we're a pure-play applied AI company, not a SaaS company with AI features hastily bolted on! We make use of the best techniques (fine tuned open source models, tool use, and retrieval augmented generation) and as a result, users send 53% of the email drafts we generate.

How has it been going?

Since launching in March 2024, we've gone from $0 to $2.5m in revenue across 3000 paid users. We did this with a team of 4, and without using any paid marketing until November.

We spent Q4 2024 in San Francisco at HF0, the best AI startup accelerator in the world, learning from experts like the CEO of Instacart, Head of Applied Research at OpenAI and the cofounder of Snowflake.

What do we value?

We're very intentional about adding new people. We think a small team of exceptional people working hard at a problem they care about will always beat a larger, more unfocused team. That means you'll need to bring an intensity to this role that might not be asked at other companies. But it also means you will be fast tracked into more senior roles and responsibilities far earlier.

We also believe in hiring people who want ownership and autonomy in their work, and giving it to them. Instead of just being handed tickets, you'll own our data infrastructure, proactively suggesting improvements, including tools we use, and how data is modelled and moved between locations.

Requirements

What will I do?

• In short, you'll own Fyxer AI's data infrastructure
• Maintain and improve pipelines between our data warehouse (Bigquery) and various data sources, including our production database, Posthog, and the SaaS tools we use - Stripe, Intercom etc, and build new pipelines
• Build improvements to our data transformation process using SQL and dbt, optimising for performance and ease of querying
• Make architecture decisions regarding data infrastructure
• Work with stakeholders in the marketing, sales, customer success and product departments to agree metrics that need to be monitored on an ongoing basis, then build data modelling to support this
• Work with data analysts to ensure data is modelled in the correct format to support data visualisation and more detailed analyses
  
  

What does our ideal hire look like?

• You've worked at an early stage tech company (<100 people) as a data engineer or similar
• You have an expert level understanding of SQL and a modern ELT stack. If you don't have both of these, you shouldn't apply
• You are excellent at transforming raw, and often complex, data into production tables for use for visualization and analysis
• You can interface with both technical and non-technical stakeholders and recommend a data structure for their area that is intuitive and easy to use
• Urgency and intensity in your work
  
  

Our tech stack

Broadly, we use a fairly typical ELT stack. It's not a requirement to have worked with every tool we use, but the more the better!

• BigQuery as our data warehouse
• Metabase for data visualization
• Fivetran to pipe raw data from third party tools (eg Stripe, which we use for billing) into our data warehouse
• dbt hosted on Github Actions for transformation of raw data into production tables ready for consumption
• Census for reverse ETL
• We use Third party tools in analysing, such as Posthog, Stripe, Hubspot, Intercom, and Meta
  
  

The application process

• Submit your CV (no need for a cover letter)
• An initial call with someone from the Fyxer AI team to review your experience and motivation for joining (15 mins)
• Live coding - SQL (30 minutes)
• Architecture interview (45 minutes)
• Meet Richard - CEO (30 minutes)

Seniority level

• Mid-Senior level

Employment type

• Full-time

Job function

• Information Technology
• Industries
• IT Services and IT Consulting

#J-18808-Ljbffr