Mid Data Practitioner (Data / Analytics Engineering - BCN/ MAD/ MUC)

Join Aily, a global company at the forefront of AI decision-making. As a Mid Data Practitioner, you will play a crucial role in building and maintaining reliable, scalable data solutions that power our enterprise-grade AI platform. You will work at the intersection of business and technology, supporting critical domains such as Finance, R&D, GTM, M&S, and Spend. Your responsibilities will include designing and implementing end-to-end data pipelines, optimizing data models for performance and reliability, and collaborating with team members on data best practices. This is an exciting opportunity for those with 2 to 4 years of experience in data engineering or analytics, a strong background in Python and SQL, and a passion for turning raw data into actionable insights.

• Design and implement end-to-end data pipelines for multiple use cases, ensuring high-quality data delivery.
• Build and maintain data components using modern data tools, including event-driven ingestion, transformations, APIs, and catalogs.
• Collaborate with team members on data craft best practices and tooling, and mentor junior/intern team members.
• Hands-on builders with:
• Data Engineers / Data Analysts with 2 to 4 years of experience who excel at building reliable, scalable data models and pipelines that power AI decision-making at enterprise scale
• Python for services/pipelines (not notebook-only analytics)
• Comfort with git, PR reviews, pytest
• Startup mindset: thrives in ambiguity, proactively solves complex data challenges, and improves tooling beyond your immediate scope
• Ready to Lead Boldly – building the data foundation that enables Aily's AI platform to deliver millions of real-time insights, briefings and agentic responses daily
• Strong SQL (complex joins, aggregations, incremental patterns)
• Interest in business domain (not just infra)
• Strong collaborators who partner with Data Practitioners, Data Scientists, Software/ML Engineers, Product teams, and business stakeholders to turn raw data into actionable customer insights