• Build and scale web scraper infrastructure • Use LLMs to structure unstructured payer policy text • Stand up CI/CD and reliability improvements across the data pipeline • Ensure that the data infrastructure runs reliably and accurately • Scale infrastructure without constant direction

• Own the design, implementation, and evolution of certified data models, semantic layers, and trusted business metrics that become the foundation for reporting and decision-making across TechnologyAdvice. • Partner directly with Finance, Revenue, Marketing, Editorial, Product, and Engineering stakeholders to understand business questions, define trusted metric definitions, and translate ambiguity into scalable data solutions. • Design, build, and maintain analytics models, transformations, and data products using modern analytics engineering practices. • Establish and continuously improve data quality through automated testing, validation, monitoring, and observability to ensure business stakeholders can trust the data they use every day. • Improve the architecture, organization, and maintainability of our analytics platform, continuously identifying opportunities to simplify, standardize, and modernize our Business Intelligence ecosystem. • Collaborate with DevOps and Software Engineers to improve data ingestion, lineage, orchestration, governance, and platform reliability across the analytics stack. • Apply AI-assisted development practices to accelerate SQL development, documentation, testing, root cause analysis, and engineering productivity while maintaining rigorous standards for data quality and accuracy. • Document business definitions, data models, metric logic, and architectural decisions to improve transparency, onboarding, and long-term maintainability. • Continuously evaluate new technologies, AI capabilities, and analytics engineering practices that improve data quality, governance, developer productivity, and stakeholder confidence. • Own trusted data products end-to-end—from business discovery and metric definition through implementation, validation, adoption, and continuous improvement. • Champion a culture of trusted data by helping teams adopt certified data models and reducing duplicate business logic across reports, dashboards, and applications.

Role Description As an Intern in the Analytics & Insights Engineering team, you will primarily work on internal technical projects that strengthen Astrafy’s data stack and ways of working. Under the guidance of consultants and your entity manager, you will contribute to both the business and technical sides of these use cases, while investing heavily in learning and developing your skills. Depending on your progress and project needs, you may also have the opportunity to contribute to selected customer engagements under the supervision of experienced consultants. You will also have the opportunity to use the latest AI technologies to support your day-to-day work on internal technical topics and accelerate how you learn, build, and deliver. On the business side: - Collaborate with internal stakeholders (consultants, Team Leads, product owners) to understand internal needs and priorities for our data and analytics stack. - Help refine and clarify requirements for internal tools, dashboards, and processes to ensure they align with Astrafy’s business objectives. - Assist in analyzing internal datasets (for example operational, financial, or product-related data) to identify opportunities for better reporting and decision-making. - Support internal workshops or working sessions to define and validate key metrics (KPIs) used across teams. - Contribute to the definition of visualizations and internal dashboards that make data easy to understand and use. - Depending on your progress and the projects in flight, you may also be involved in specific, well-scoped client-related tasks, mainly as support to a consultant. On the technical side: - Contribute to building and improving internal data models in BigQuery and dbt that support Astrafy’s own reporting and analytics needs. - Help develop and maintain LookML models and internal dashboards used by the Astrafy team. - Implement and test simple business rules and data transformations to ensure internal data is consistent, reliable, and well documented. - Support the creation of internal reports and monitoring dashboards with a strong focus on usability and clear data presentation. - Explore and, where relevant, leverage AI-driven solutions to improve internal workflows and user experience. - Depending on your progress and the projects in flight, you may also be involved in specific, well-scoped tasks on client projects, always under the guidance of a senior consultant. In addition to these responsibilities, we strongly value knowledge-sharing and community engagement. At Astrafy, we are active contributors on platforms like Medium, where we regularly publish articles to share our expertise, insights, and innovations. As part of the role, you will be encouraged to contribute to articles and internal documentation, sharing what you learn and helping to grow Astrafy’s impact and knowledge base. Learn fast, deliver small but reliable pieces of work, and progressively gain autonomy with the support of the team. Your success is measured by your ability to learn, collaborate, and ship incremental value with quality and consistency. Qualifications - Basic experience with enterprise-grade BI or analytics tools is a strong plus (Looker, Data Studio, or similar). - Solid understanding of SQL fundamentals; you can write simple queries and are eager to improve. - Initial exposure to Google Cloud (BigQuery, Cloud Storage, Cloud Functions, etc.) is appreciated. - Basic understanding of dbt and end-to-end data pipeline concepts (ETL/ELT). - Familiarity with Git and collaborative development workflows is a plus. - Strong interest in data visualization and the ability to build basic, clear reports with guidance. - Attention to detail and quality, with a structured and solution-oriented mindset. - Strong willingness to learn, take feedback, and continuously improve. - Curiosity about both the business context and the technical stack. - Reliability in executing tasks agreed with your mentor or consultant and communicating clearly about progress and blockers. - Team mindset: ready to help with supporting tasks such as testing, documentation, and small fixes. - You work well in a team and can progressively handle tasks independently as you gain experience. - Excellent communication and collaboration skills (with colleagues and, when relevant, with external stakeholders). - You speak Spanish and English fluently. Speaking French is a significant plus and will help you stand out from other applicants. Benefits - Be part of a dynamic team that shares strong values. - Continuous training and development of both soft and hard skills. - Flexible working hours and remote policies. - An excellent team of colleagues and regular team-building activities. - And much more. We have compiled a detailed handbook covering all you need to know about our company. Company Description At Astrafy, we are on a mission to empower every individual with a truthful vision of reality. By acting as a seamless extension of our clients' Data & AI departments, we engineer the robust foundations required to strip away bias and replace guesswork with certainty. We prioritize true craftsmanship over generic consulting, bringing strict software engineering rigor to the rapidly evolving data and Generative AI landscape. Ultimately, we don't just build technology — we democratize insights, enabling organizations to make confident, fact-driven decisions that unlock their highest potential.

• Build and maintain a unified data collection platform using Golang, Kubernetes, ClickHouse, and NATS, with success measured by reliability, performance, and adoption across GitLab teams. • Help improve the emission, transit, and quality of instrumentation data across the system, with success measured by data quality, data integrity, and usage billing accuracy. • Support usage billing and instrumentation systems during working hours, with success measured by timely incident handling and reliable service restoration. • Work across the organization with research and development teams and the enterprise data organization to deliver common solutions, with success measured by adoption across multiple stakeholders. • Contribute to technical choices that balance ease of use for product teams with reliability and correctness for data consumers, with success measured by platform usability and trusted downstream data.