Data Analyst

Analyze large and complex datasets to identify trends, opportunities and business insights using Google BigQuery;

Develop scalable data pipelines, automated reporting solutions and analytical models using Python and Airflow;

Work with GCP services, primarily BigQuery and Looker, to build and optimize analytics workflows;

Design and maintain dashboards, KPIs and self-service reporting solutions for business stakeholders;

Translate business requirements into analytical solutions and actionable recommendations;

Collaborate with engineering, product and operations teams to support data-driven decision-making;

Perform data validation, quality checks and root-cause analysis to ensure data integrity;

Optimize SQL queries and improve performance of large-scale data transformations;

Support automation and AI-driven initiatives by leveraging modern analytics and data engineering practices;

Document analytical processes, data models and reporting frameworks to ensure maintainability and scalability.

Bachelor’s degree in Data Analytics, Computer Science, Statistics, Mathematics, Engineering or a related field;

3+ years of experience in data analytics, business intelligence or analytics engineering roles;

Advanced proficiency in Python for data analysis, automation and workflow development;

Strong SQL skills and experience working with large-scale relational and analytical databases;

Hands-on experience with Google Cloud Platform (GCP), particularly BigQuery and Looker;

Strong understanding of data modeling, ETL/ELT processes and analytics engineering concepts;

Experience working with orchestration and automation tools such as Airflow;

Experience working with version control systems such as Git;

Excellent communication and stakeholder management skills, with the ability to present insights to technical and non-technical audiences;

English proficiency is mandatory;

Curious mindset, proactive attitude and strong collaboration skills.

Exposure to Data Science and Machine Learning concepts, including predictive modeling and statistical analysis;

Familiarity with modern AI such as Natural Language Processing (NLP), Large Language Models (LLMs) and deep learning frameworks;

Knowledge of machine learning libraries and frameworks such as Scikit-learn, TensorFlow or PyTorch;

Experience working in Agile environments and using tools such as Jira and Confluence.