Remote Analytics Engineer
Blueground
πRemote - Brazil
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Job highlights
Summary
Join Blueground's growing team as an Analytics Engineer to transform raw data into actionable insights, enabling the company to make informed decisions that drive business growth.
Requirements
- Bachelorβs degree in Computer Science, Data Science, Engineering, Economics, Statistics or a related field
- 2+ years of experience in a data engineering, analytics engineering, or a similar role
- Proficiency in SQL and experience with data modeling tools
- Knowledge in Python for data analysis and extraction
- Experience with ETL tools (e.g., Airflow)
- Strong understanding of data warehousing concepts and technologies (e.g. BigQuery or similar)
- Experience with Google Looker Studio or similar tool
- Familiarity with cloud platforms (e.g., AWS, Google Cloud) is a plus
- Familiarity with Scrapers, APIs, DBT and Git are a plus
- Strong problem-solving skills and attention to detail
- Excellent communication and collaboration skills
Responsibilities
- Design, develop, and optimize data models to support reporting, analytics, and business intelligence
- Maintain and update existing data models as the business evolves
- Develop, test, and maintain ETL (Extract, Transform, Load) processes to ensure the accurate and timely flow of data from various sources into data warehouses
- Collaborate with data engineering to ensure the quality and reliability of data pipelines
- Oversee the management and optimization of data warehouses to support analytics and reporting requirements
- Ensure data warehouses are scalable, reliable, and secure
- Design and develop dashboards and reports using Google Looker Studio to provide actionable insights to stakeholders
- Ensure that dashboards are user-friendly and meet the needs of various business units
- Work closely with business stakeholders to understand their data needs and translate them into technical requirements
- Stay updated on the latest trends and technologies in data engineering and analytics
- Continuously seek opportunities to improve data processes, tools, and methodologies
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