Senior Data Engineer (AI)

TL;DR

Senior Data Engineer (AI): Build and maintain data infrastructure components (pipelines, transformations, APIs, catalogs) that power an AI decision intelligence platform with an accent on multi-tenant scalability, reliability (24/7), and data quality/governance. Focus on designing end-to-end data systems that deliver trustworthy datasets for ML and real-time enterprise insights.

Location: Madrid (MAD), Barcelona (BCN) and Munich (Hybrid: 2 days per week in the office). Applicants must already have the legal right to work in Spain or Germany.

Company

hirify.global builds a B2B SaaS decision intelligence app that combines enterprise data with machine learning, large language models, and agentic AI.

What you will do

• Build and maintain streaming pipelines, transformations, APIs, and data catalogs using modern data tools.
• Implement data quality frameworks, monitoring, and alerting systems at scale.
• Optimize data workflows for cost, performance, and reliability across multiple datasets.
• Design and implement complex, scalable data platforms and pipelines supporting multiple teams and use cases.
• Own critical data systems end-to-end, from requirements to production monitoring, with strategic guidance only.
• Collaborate with ML Engineering, Data Science, and Product to define data architecture that powers AI at scale.

Requirements

• 4+ years of experience building and operating data infrastructure and pipelines.
• Hands-on experience with data engineering components such as pipelines, transformations, APIs, and data catalogs.
• Strong ability to ensure reliability and data quality (validations, monitoring, governance) for production systems.
• Experience optimizing data workflows for performance and cost in multi-dataset environments.
• Ability to mentor junior team members on data engineering best practices and tooling.
• Legal right to work in Spain or Germany.

Culture & Benefits

• Hybrid work policy: 2 days per week in the office.
• Data team owns the full data lifecycle (ingestion, transformation, quality, delivery) as a core product capability.
• Emphasis on reliability, governance, and “single source of truth” data principles.
• Code-first modern data stack and rigorous PR-based review process.

Hiring process

• Interviews focused on data engineering experience, system design, and production reliability/quality practices.
• Technical evaluation of your approach to building scalable data platforms and pipelines.