The ideal candidate should have strong problem-solving skills and experience working with large datasets in real-world applications.

Key Responsibilities:

- Write efficient, scalable Python code for data processing and transformation.

- Perform complex data manipulation using Pandas, NumPy, and other scientific libraries.

- Clean, transform, and analyze large volumes of structured and unstructured data.

- Develop scripts and tools to automate workflows.

- Collaborate with data engineers and analysts to optimize data pipelines.

- Participate in code reviews and follow best practices in version control (Git).

Required Skills:

- Strong proficiency in Python, especially with Pandas and NumPy.

- Experience working with data files in formats like CSV, Excel, JSON, and SQL.

- Familiarity with Jupyter Notebooks, API integrations, and data visualization tools (e.g., Matplotlib, Seaborn).

- Experience with SQL queries and relational databases.

- Good understanding of software development lifecycle and version control (Git).

Good to Have:

- Exposure to machine learning libraries (e.g., scikit-learn).

- Experience with cloud platforms (AWS/Azure/GCP) or data lake frameworks.

- Familiarity with Airflow, Spark, or other data orchestration tools.