Are you ready to take part in the upcoming AI revolution of the business world? If the answer is a resounding "yes," we have an extraordinary opportunity for you!
About Us
Aily Labs is a B2B SaaS company building an AI-powered decision intelligence app for enterprises. Our mobile-first platform combines company data with advanced machine learning, large language models, and agentic AI to give business leaders clear, actionable insights in real time.
Instead of traditional dashboards and reports, Aily delivers simple, personalized recommendations that help teams decide and act faster across finance, supply chain, R&D, commercial, and more. The app is designed to be intuitive for end users and easy to integrate for IT, so companies can move from analysis and indecision to confident, data-driven decisions every day.
Founded in 2020 in Munich, Aily Labs has grown into a global team of 350+ people across Munich, Barcelona, Madrid, Cluj, and New York. Our mission is to democratize AI in business and enable leaders to Lead Boldly, turning complex data into decisions that drive real, measurable impact.
We are looking for Data Practitioners (Mix of Data Engineer and Analytics skills) who are passionate about building reliable, scalable data solutions that power enterprise-grade AI: people who make data accessible, trustworthy and ready for ML, ensuring our decision intelligence platform can serve millions of insights every day.
About this team – Data Chapter
What does our Data Chapter team do?
We are the backbone of Aily’s "Data-to-Decision" engine. We build and own the complete data lifecycle—from ingestion and transformation to quality and delivery. Our team develops the robust models, APIs and scalable infrastructure that ensure high-quality data is always available for the Aily App and our AI models.
Which data domains and products do we support?
We operate at the intersection of business and technology, supporting critical domains including Finance, R&D (Research and Development), GTM (Go-to-Market), M&S (Manufacturing & Supply), Spend…. Our pipelines power the Aily App across multiple tenants, delivering the insights that drive global enterprises in several industries.
What are our key technical challenges?
Scale: Managing multi-tenant by design with config-driven pipelines and models that adapt to diverse client needs.
Reliability: Ensuring 24/7 uptime through automated QA, validations, and proactive monitoring.
Quality: Implementing sophisticated business logic checks to maintain a "Single Source of Truth."
Governance: Orchestrating complex event-driven and scheduled flows while maintaining strict data security and compliance.
How do we collaborate?
Data is our product. We work as strategic partners:
With Product: To define and refine features that solve real-world problems.
With Software Engineering: Establishing clear Data Contracts to ensure seamless integration with the Aily App.
With ML & Data Scientists: Engineering the high-performance datasets required to transform raw data into measurable AI impact.
Our Tech Stack
We leverage a modern, code-first data stack to maintain agility and high standards:
Languages & Frameworks: Python is our core for pipelines, APIs, and CLI tooling. We use FastAPI and Pydantic to serve typed, high-performance REST APIs to our platform.
Data Transformation & Modeling: We use dbt for our SQL-based transformation layer and SQLModel with Alembic for ORM-based schema management and versioned migrations.
Storage & Analytics: Besides PostgreSQL / RDS, we utilize DuckDB and DuckLake as our embedded analytics engine, all hosted on a robust AWS infrastructure (S3, IAM).
Orchestration & Workflow: Apache Airflow handles our complex event-driven and scheduled job flows.
Quality & Engineering Excellence: We prioritize reliability through pytest for QA validations, GitHub for version control, and a rigorous PR-based code review process.
End-to-End Architecture & Business Ownership: Lead the design, implementation, and long-term ownership of complex, multi-tenant data platforms with minimal strategic guidance, deeply aligning technical solutions with business domains and use cases.
Core Development & Tooling: Build robust data components, utilizing advanced Python for APIs/orchestration and expert SQL for dbt-based transformation and normalization layers.
Data Quality & Engineering Standards: Establish and enforce global data engineering standards, implementing shift-left data quality frameworks, validation, and proactive alerting at scale.
Infrastructure Optimization: Drive continuous improvements in data infrastructure, modeling, and tooling to optimize performance, control costs, and reduce operational burden.
Mentorship & Craft Excellence: Champion engineering excellence across the chapter, mentoring junior team members and defining best practices for tooling and data craft.
Data Engineers / Data Analysts with 4 to 6 years of experience who excel at building reliable, scalable data models and pipelines that power AI decision-making at enterprise scale.
Hands-on builders with:
Expert SQL skills (architecting advanced incremental patterns, complex modeling, and query optimization)
Advanced Python for services/pipelines (not notebook-only analytics)
Deep familiarity with production-grade engineering practices, including rigorous CI/CD pipelines, test-driven development (pytest), and leading technical code reviews.
A strong business-domain mindset—the ability to act as a translator between complex business data needs and highly efficient data architectures.
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.
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.
Locations: Madrid (MAD), Barcelona (BCN) and Munich (MUC).
Contract type: Permanent.
Work policy: hybrid (2 days per week in the office).
