Senior Data Engineer

MITO AI; Madrid; On-site; EUR 60,000 - 80,000; Full time

Job summary

An innovative AI-native platform in Madrid is seeking a skilled individual with over 5 years of experience to build distributed backend infrastructure and data architecture. The role involves designing pipelines for AI outputs, ensuring real-time collaboration, and optimizing performance for creative workflows. The ideal candidate embraces a startup mindset and is curious about AI-native systems. This position offers a unique opportunity to shape the foundations of the creative collaboration experience in a growing industry.

Qualifications

• Experience with media processing pipelines is a strong plus.
• 5+ years of experience in building distributed backends or data infrastructure.
• Strong understanding of event-driven architectures.

Responsibilities

• Design ingestion and processing pipelines for user events and AI outputs.
• Architect event-driven systems that support real-time interactions.
• Build scalable pipelines for high-volume creative assets.

Skills

5+ years building distributed backend or data infrastructure

Experience with event-driven architectures and high-throughput systems

Strong schema design and data modeling intuition

Comfortable working with unstructured and semi-structured data

Startup mindset: pragmatic, fast, accountable

Curious about AI-native systems and creative workflows

Tools

Kafka or event streaming systems

Job description

MITO AI is a collaborative, AI-native platform reinventing how films, commercials, and music videos are made. We are building the operating system for a $300B+ global video production industry as it shifts to AI-native workflows.

MITO was founded by Iñaki Berenguer (Master MIT, PhD Cambridge, 5x founder and CEO, founded social photo startup Pixable acquired by SingTel, founded of CoverWallet $300M exit in 4 years, founded AI infrastructure company iPronics, which has raised $50M) & Danny Saltaren (award-winning product designer at 2 tech unicorns, National Design Award recipient) and Arantxa Barcia (Art Director).

We are backed by Lightspeed Venture Partners and investors including Kibo, Kfund, Sequoia and a16z scouts, LifeX, Everywhere, 5 unicorn founders, and execs from Github and Roblox.

Role Overview

This role owns the data backbone that makes that possible.

You will design and scale the event, asset, and metadata architecture that powers creative collaboration, AI orchestration, and real-time editing. This is not generic CRUD backend work — this is building the foundation of a next-generation creative system.

What You Will Own

• Design ingestion and processing pipelines for user events, AI outputs, asset metadata, and collaboration signals
• Architect event-driven systems that support real-time canvas interactions and timeline edits
• Define schemas for scenes, assets, storyboards, prompts, model outputs, and versioning
• Ensure traceability across multi-model AI workflows

Infrastructure & Performance

• Build scalable pipelines for high-volume creative assets (images, video, audio, embeddings)
• Optimize storage, indexing, and retrieval across structured and unstructured data
• Maintain performance across collaborative sessions and multi-user editing
• Balance cost efficiency with low latency

Observability & Reliability

• Implement monitoring and logging across AI calls, orchestration layers, and asset pipelines
• Ensure reproducibility and lineage of generated outputs
• Design systems that tolerate model variability and partial failures
• Work closely with AI engineers to expose structured data for orchestration and retrieval
• Partner with frontend engineers to optimize real-time synchronization
• Support product in defining metrics across activation, engagement, and creative output quality

Technical Stack

• Kafka or event streaming systems

Experience with media processing pipelines is a strong plus.

Who We Are Looking For

• 5+ years building distributed backend or data infrastructure
• Experience with event-driven architectures and high-throughput systems
• Strong schema design and data modeling intuition
• Comfortable working with unstructured and semi-structured data
• Startup mindset: pragmatic, fast, accountable
• Curious about AI-native systems and creative workflows

What Success Looks Like

• Creative workflows feel instant and reliable
• AI outputs are traceable and reproducible
• Collaboration works in real time without breaking
• Data architecture scales as usage grows
• Engineering moves faster because the foundations are solid