Who We Are

Maki is transforming HR with fully autonomous AI hiring agents that handle everything from sourcing and screening to interviewing and selection at scale. Instead of being stuck in administrative tasks, HR teams can now focus on what really matters: engaging top talent, making smarter hiring decisions, and driving business success. Companies like H&M, BNP Paribas, and FIFA already trust Maki to automate 90% of screening and interviewing, reducing time-to-hire by 3x and turnover by 20-30%.

Maki isn’t just another tool, it’s the future of AI-powered talent acquisition.

Your Mission at Maki ⚡

Data Engineer

As a Data Engineer at Maki, you will build the data foundation that powers our AI agents, product analytics, and customer insights. Your mission is to turn raw data into clarity, enabling every team at Maki to make better decisions, unlock product opportunities, and deliver a magical experience to customers.

You will:

• Build and maintain the core data pipelines and models that fuel product and business insights.

• Define how we collect, structure, and track product data so we can deeply understand user behaviour and product performance.

• Create clean, reliable, self-serve data assets that empower Product, Customer Success, and Engineering to explore and answer questions rapidly.

• Help standardize our metrics and data language, making sure the organization operates from a single source of truth.

• Champion data reliability and quality, ensuring our systems scale with accuracy, transparency, and trust.

We would love to work with you if…

You might be a strong fit if you:

• Have excellent SQL skills and experience modeling analytics-ready datasets

• Have worked with modern data stacks (dbt, cloud warehouses, ELT pipelines)

• Are comfortable owning end-to-end data foundations in a fast-moving environment

• Think in terms of product impact: clarity, usability, reliability, and speed

• Are a strong communicator who can collaborate with engineering, product, and customer teams

• Care about building simple, elegant systems that scale pragmatically