Data Analytics Engineer

About the role

You will act as the bridge between raw data and business impact, building and maintaining scalable data pipelines and robust data models. Your work will enable teams across the organization to access clean, reliable data and make data‑driven decisions.

Key responsibilities

• Design, develop, and own end‑to‑end data pipelines from ingestion to consumption using tools such as Airflow, Kubernetes and AWS services.
• Create advanced data models in SQL and Python, integrate new data sources, and ensure data quality.
• Provide self‑service analytics capabilities through AWS QuickSight and other visualization tools.
• Collaborate with product, engineering, and business stakeholders to define event tracking requirements and translate them into data solutions.
• Perform ad‑hoc analyses and generate data sets to answer business questions.
• Train and deploy large language models (LLMs) to support internal teams and customers.

Required profile

• Several years of experience in data modeling or data engineering.
• Proven ability to work with cross‑functional teams and translate business needs into technical solutions.
• Strong organizational skills and the capacity to prioritize tasks in a dynamic environment.
• Effective communication of complex technical concepts to both technical and non‑technical audiences.

Required skills

• Python
• SQL
• Git / GitHub
• Docker
• Apache Airflow
• Kubernetes (k8s)
• AWS QuickSight
• Metabase, Looker, Tableau
• Machine Learning and Large Language Models (LLMs)
• Data modeling and pipeline development

What we offer

• Culture of open communication, regular feedback and respect.
• Freedom to contribute ideas and creativity.
• International, dynamic work environment backed by industry partners.
• Flexible remote policy with the possibility to work from anywhere for up to six weeks per year.