GM02 - Data Engineer
The client is building an AI-native data platform for professional sports organizations, helping teams transform massive volumes of performance, recruitment, tactical, and medical data into actionable intelligence. Their platform unifies data from multiple providers—including tracking systems, wearables, event feeds, and scouting sources—into a scalable ecosystem that powers analytics, player evaluation, recruitment workflows, and AI-driven decision-making. Trusted by professional organizations, the platform enables technical staff to spend less time managing data infrastructure and more time generating competitive insights. By combining modern data engineering, machine learning, and Generative AI technologies, the company is creating the next generation of sports intelligence products used by analysts, scouts, coaches, and sporting directors.
About the Project
We are supporting a fast-growing AI company building a modern data platform that powers advanced analytics, machine learning, and AI-driven decision-making for enterprise applications. The platform ingests and processes large volumes of structured and semi-structured data from multiple external providers, transforming them into reliable datasets that support analytics, predictive models, and customer-facing products. Operating in a high-performance, data-intensive environment, the engineering team focuses on building scalable, production-grade data infrastructure that enables both traditional analytics and emerging AI workflows such as feature engineering, retrieval systems, and LLM-powered applications. The engineering culture values software engineering excellence, maintainability, automation, and ownership, with a strong emphasis on cloud-native architecture, modern data platforms, and collaborative product development.
About the Role
We are looking for a Data Engineer with a strong software engineering mindset to help build and scale a modern cloud-native data platform. This is a hands-on engineering role focused on designing, developing, and maintaining reliable data pipelines and foundational infrastructure that powers analytics, machine learning, and AI applications. You'll collaborate closely with software engineers, data scientists, analytics teams, and product stakeholders to build robust frameworks that enable data processing, experimentation, and production AI workloads. The ideal candidate enjoys solving complex engineering problems, writing clean and maintainable code, and building scalable systems that support both data products and modern AI initiatives.
Role and Responsibilities
- Design, build, and optimize scalable data pipelines for ingesting, transforming, and serving large-scale structured and semi-structured datasets.
- Develop reliable ETL/ELT workflows using Python, SQL, Apache Spark, and modern data engineering practices.
- Build scalable data models and storage architectures that support analytics, machine learning, and customer-facing applications.
- Implement robust data engineering patterns, including partitioning strategies, rollback-capable migrations, schema evolution, and fault-tolerant processing.
- Develop and maintain production-grade data workflows using CI/CD and software engineering best practices.
- Monitor, troubleshoot, and optimize pipeline performance, reliability, and data quality.
- Partner with software engineers to integrate data infrastructure into production products.
- Collaborate with data scientists to support feature engineering, experimentation, and model development workflows.
- Contribute to modern AI infrastructure supporting vector search, Retrieval-Augmented Generation (RAG), and LLM-based applications.
- Improve engineering standards around testing, documentation, observability, governance, and platform reliability.
What We're Looking For
Must-Haves
- B2/C1 English level
- 2+ years of experience as a Data Engineer or Software Engineer working with data platforms
- Strong programming experience with Python
- Solid experience writing advanced SQL
- Experience building production ETL/ELT pipelines
- Experience with Apache Spark
- Understanding of relational databases and data modeling concepts
- Experience with software engineering best practices, including Git, CI/CD, testing, and production support
- Familiarity with cloud-based data platforms
- Strong problem-solving skills and collaborative mindset
Nice-to-Haves
- Experience with Databricks, Snowflake, or similar modern data platforms
- Experience with dbt
- Knowledge of dimensional modeling and scalable data warehouse architectures
- Experience with data quality frameworks such as Great Expectations or Soda
- Exposure to machine learning pipelines, feature engineering, or MLOps workflows
- Experience building infrastructure for Generative AI applications, including vector databases, Retrieval-Augmented Generation (RAG), embeddings, chunking strategies, or LLM-powered systems
- Familiarity with cloud platforms such as AWS, Azure, or GCP
- Interest in analytics, AI, or data-intensive applications
Why This Role Is Exciting
- Build the data foundation powering next-generation AI products
- Work across modern data engineering, machine learning, and Generative AI initiatives
- Collaborate closely with software engineers, data scientists, and product teams
- Influence architecture decisions and engineering best practices from an early stage
- Work with modern technologies including Spark, cloud-native data platforms, and AI infrastructure
- Join a collaborative engineering culture focused on innovation, ownership, and continuous learning
- Opportunity to solve complex large-scale data engineering challenges with real-world impact
Application Instructions
Submit everything here:
- Your résumé/CV highlighting your Python, SQL, Spark, and data engineering experience
- Links to GitHub, technical projects, or portfolio (if available)
- Your availability and compensation expectations
- Any experience building production data platforms, machine learning infrastructure, or Generative AI systems