Junior Data Practitioner, Aily Labs

• We’re looking for a data practitioner who is passionate about building reliable data foundations
• Technical stack: Strong SQL skills and hands-on experience with ETL/ELT tools and Python for data workflows. Comfortable with data warehouses/lakes and version control
• Data quality mindset: You believe that “garbage in, garbage out” is real, and you are obsessed with validating and ensuring the trustworthiness of data
• Business & product sense: You don’t just move data blindly — you care about why it matters
• You seek to understand the business context behind the data and ensure your pipelines serve actual product and business needs
• Builder & optimizer: You are persistent and curious, breaking down complex data problems into shippable steps, iterating quickly, and continuously improving systems
• Technical excellence: You care about clean, maintainable, and well-documented code, and you follow best practices in testing, monitoring, and CI/CD for data
• Communication & collaboration: You can clearly explain data issues and trade-offs to non-technical stakeholders. You work cross-functionally to align on requirements and deliver the right solution
• Startup mindset: Comfortable with ambiguity and open-ended problems. Autonomous and proactive: when you see a broken pipeline or unclear requirement, you fix it or propose a better way

Desirable

• You’ll be the person ensuring that our pipelines are robust, data is validated, and stakeholders can trust the insights and AI models that depend on it
• More than just moving data, you’ll care deeply about data quality, business context, and technical excellence
• Work on high-impact AI problems with forecasting, classification, regression, recommendations, LLMs or agents
• Learn how to take ideas from inception to production, having an impact on Fortune-500 companies
• Collaborate with talented Data Scientists, ML Engineers, Data Practitioners, Software Engineers, PMs and domain experts
• Design, build, and maintain ETL/ELT pipelines that are scalable, efficient, and easy to monitor
• Implement data validation and quality checks, ensuring datasets are consistent, accurate, and production-ready
• Collaborate with data scientists, ML engineers, PMs, and domain experts to understand data needs and translate them into solid pipelines
• Debug issues in pipelines and proactively prevent future problems by improving observability and documentation
• Balance business needs (delivering the right data fast) with technical excellence (ensuring long-term maintainability)