At CI&T, we help large enterprises transform the potential of AI into real business impact with AI Deployment, AI-native execution, and tech-integrated business solutions.
With 30 years of experience in technological transformation, we accelerate innovation with expertise in Agentic SDLC, Application modernization, Data & AI, Martech and Business strategy.
We are 8,000 CI&Ters across more than 25 countries, collaborating to build solutions with real impact. AI is already part of how we work, evolve, and innovate every day.
The Analytics Engineer will contribute to the design, development, maintenance, and evolution of AI and Data Products.
Required Experience
- Expertise in creating and managing data transformation pipelines using DBT.
- Advanced knowledge of SQL for data querying, transformation, and aggregation.
- Strong coding skills in Python, especially with libraries related to data manipulation (e.g., Pandas).
- Experience in applying good software engineering practices such as testing, clean code, code formatting, and peer review.
- Ability to design scalable and robust data pipelines.
- Proficiency in Azure Data Lake, Azure Data Factory, Azure Blob Storage, Azure SQL Database, and other related Azure services.
- Working knowledge of Azure Databricks for deploying & running Spark jobs.
- Prior knowledge of PySpark for data processing, particularly DataFrame API.
- Understanding of Delta Lake for reliable data lakes.
- Knowledge of both relational (SQL Server) and NoSQL databases (like Cosmos DB).
- Proficiency in tools like Python libraries (e.g., Pandas, NumPy) and data analysis platforms (e.g., Jupyter Notebooks).
- Experience in cleaning, transforming, and preparing raw data for analysis and modeling.
- Proficiency in data visualization with Power BI for creating impactful visualizations and dashboards.
- Understanding of role-based access controls and integration with Azure Active Directory.
- Experience with continuous integration and continuous deployment tools like Azure DevOps.
Desired Knowledge
- Knowledge of tooling in the Python ecosystem such as dependency management tooling (poetry, venv).
- Ability to create interactive dashboards and reports to communicate analytical insights effectively.
- Knowledge of both relational (SQL Server) and NoSQL databases (like Cosmos DB).
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