MID Data Practitioner - BCN/MAD/Munich

About the role

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 300+ 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 MID Data Practitioners who are passionate about building reliable, scalable data infrastructure that powers AI at scale: people who make data accessible, trustworthy, and ready for ML, ensuring our decision intelligence platform can serve millions of insights every day. Your job is to build the data foundation that makes Aily's AI possible. About this team - Data Practitioners Data team overview & challenges 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 robust 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, GTM (Go-to-Market), M&S (Management & 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, config-driven pipelines 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 Scientist : 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: 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. Core responsibilities 2-4 years of experience Design and implement end-to-end data pipelines (ingestion → transformation → quality → delivery) for multiple use cases with growing autonomy. Build and maintain data infrastructure components (streaming pipelines, transformations, APIs, catalogs) using modern data tools. Implement robust data quality frameworks, monitoring, and alerting systems at scale. Optimize data workflows for cost, performance,e and reliability across multiple datasets. Mentor junior team members on data engineering best practices and tooling. Who we're looking for (all levels) Data Engineers who excel at building reliable, scalable data infrastructure that powers AI decision-making at enterprise scale. Hands-on builders who love creating robust data pipelines, optimizing systems for performance and cost, and ensuring data quality at every step. Strong collaborators who partner with Data Scientists, ML Engineers, Product teams, and business stakeholders to turn raw data into actionable insights. Startup mindset: thrives in ambiguity, proactively solves