Data Engineer

Responsibilities:

• Build and maintain data pipelines to collect, clean, and transform data from different sources into structured datasets suitable for analysis and reporting.

• Work with Python and Databases to automate data extraction, preprocessing, and feature engineering for real-world projects such as financial modeling and analytics.

• Support data integration between APIs, databases, and spreadsheets, ensuring data quality, consistency, and reproducibility.

• Develop basic data models and documentation to help data scientists and analysts use the data effectively.

• Monitor pipeline runs, debug data issues, and improve performance and reliability over time.

• Collaborate with business and research teams to understand data requirements and deliver practical data solutions.

Requirements:

• Master’s degree in Business Information Systems, Computer Science, Data Science, Statistics, or a related field (current or recent graduate).

• Must have some experience working with real-world data (e.g., surveys, financial data, operational logs, or external datasets) in projects, internships, or work.

• Hands-on experience with Python and SQL, including data preprocessing, analysis, and simple ML pipelines (using tools like Pandas, NumPy, Scikit-learn, XGBoost).

• Interest in both data engineering (pipelines, integration, quality) and data science (analytics, modeling, experimentation).

• Familiarity with data visualization or BI tools and basic statistical analysis.

• Some project experience building end-to-end data workflows, such as automating data collection, cleaning, and model training or reporting.

• Good analytical and communication skills; able to explain data findings and document processes clearly.

• Interest in working on complex, real-world datasets and supporting AI/ML or research applications.