This Senior Data Engineer position requires a comprehensive skill set in data science, machine learning, and data engineering. Here’s a breakdown of the key qualifications and responsibilities:

Skills:

• Python Expertise: Strong programming in Python for data science and machine learning.
• Data Science Libraries: Experience with libraries such as Matplotlib, Pandas, NumPy, and Scikit-learn.
• ETL: Experience with Extract Transform Load (ETL) processes
• Data Analysis Tools: Have Data Analytics skills. Expert in Jupyter Notebooks, Google Colabs, and Kaggle.
• Data Visualization: Expertise in Microsoft Power BI for creating interactive dashboards and reports.
• Data Modelling: Proficient in Excel formulas and functions (e.g., VLOOKUP, INDEX-MATCH, pivot tables, and macros).
• SQL Knowledge: Knowledge of SQL for querying and managing data from various sources.
• Cloud Platforms: Experience with cloud platforms such as Azure or AWS is a plus.
• Data Scraping and API: Expertise in data scraping and working with API technologies.

Responsibilities:

• Team Supervision: Supervise a junior data engineer.
• Data Pipeline Development: Assist in the design, development, and maintenance of data pipelines and workflows.
• Data Collection and Cleaning: Collect, clean, and transform data from various sources, including APIs, web scraping, or surveys.
• Data Analysis: Analyze and interpret data using descriptive and inferential statistics, machine learning, or other methods.
• Data Product Delivery: Create and deliver data products, such as dashboards, reports, and visualizations, using Microsoft Power BI, Excel, and Python.
• Decision Support: Provide data insights and recommendations to support decision-making and problem-solving for various projects and clients.
• Collaboration: Collaborate with other data engineers, analysts, and stakeholders to ensure data quality and consistency.