Data Engineer - 55,000-90,000 GBP
London, England, United Kingdom
V7Labs
The full infrastructure for enterprise training data covering labeling, workflows, datasets, and humans in the loop.If you're not familiar with V7, here's what we do: https://youtu.be/iBpgSQk5Qyg
V7 is an AI data platform to automate any visual task, voted by Forbes as one of the top 25 machine learning startups of 2021. We have raised $10 million in venture funding and are backed by some of the most competent individuals in AI. We manage the training data and models of hundreds of AI companies and enterprises. What sets us apart is our team's obsession with pushing our product to where AI will be three years from today.
About the role:
As a lead data engineer at V7 you will be building our internal business intelligence engine from ground up, powering critical business decisions with data. You will collaborate across the business, including with our world class engineering team. You are technically confident, with a knack for analytics. You are able to work with decision makers to understand the “why” behind data requests. You’re a creative problem solver and systems-thinker and leverage this when transforming ambiguous requests into solid action plans. You’ve a point of view on what constitutes best in class data stack (eg Snowflake, Airflow, dbt, EKS, etc.) and can lead decision making with team leaders.
What you'll be doing
- Design, deploy, own and maintain best-in-class data infrastructure
- Build and maintain our ingestion pipelines from both internal databases and external sources (eg. SaaS applications) into our warehouse/data lake.
- Build and maintain internal tooling interactive dashboard layer(eg. Retool) so both commercial and technical team members can query data to answer business questions.
- Create and maintain architecture and systems documentation
- Write maintainable, performant code
- Plan and execute system expansion as needed to support the company's growth and analytic needs
- Generate architecture recommendations and the ability to implement them
- Build out the roadmap on how the data stack evolves with the team
- Collaborate with other team leads to prioritise and scope data goals (eg.a new metric or KPI) and develop implementation approach to achieve their data goals
- Prioritize between multiple planned analytics projects and ad-hoc data requests for our internal stakeholders.
- Create smaller merge requests and issues by collaborating with stakeholders to reduce scope and focus on iteration, ship medium to large features independently
- Work with various business and engineering teams to ensure reliable, scalable, robust architecture for our internal data platform and how it fits into the wider V7 architecture.
Requirements
- 5+ years hands-on experience deploying production quality code
- Experience with Python or Java for data processing (Python preferred)
- Demonstrably deep understanding of SQL and analytical data warehouses (Snowflake or Databricks preferred)
- Hands-on experience implementing ETL (or ELT) best practices at scale and data pipeline tools (Airflow, dbt etc)
- A curious, scientific mind.
- Fluent in English
Interview Process
- Stage 1 - Screening call
- Stage 2/3 - Technical call & challenge
- Stage 4 - Final & offer
Benefits
- Unlimited vacation, just tell us when you need time off.
- Stock options
- Work from anywhere
- 7-day company retreats in stunning locations
- New Apple hardware
- Paid tickets, accommodation, and travel to relevant conferences, nationally or internationally (NeurIPS, ICCV, CVPR, [...]) to expand your network & knowledge during normal times.
- Central London office with standing desks and 4K monitors.
- Unlimited high-quality coffee, tea, snacks, and other comforts every day.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Airflow Business Intelligence Databricks ELT Engineering ETL Machine Learning NeurIPS Pipelines Python Snowflake SQL
Perks/benefits: Career development Conferences Equity Startup environment Team events Unlimited paid time off
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