Mid Data Analyst - Oil, Fuels and Chemicals Market Insights (ETL) - en Procalidad Sas

Mid Data Analyst - Oil, Fuels and Chemicals Market Insights

Are you passionate about using data to uncover trends and drive smarter decisions? Do you thrive in an environment where analytical skills meet real-world market dynamics? Join our team as a  Data Analyst  and play a key role in shaping insights for the ever-evolving oil, fuels, and chemicals market.

  Bachelor’s degree  in Chemical Engineering, Mathematics, Economics, Data Science, or a related field.
  Minimum of 3 years of experience  as a data analyst or similar role, preferably within the  oil, gas, or commodities  industry.

  Programming:  Strong proficiency in  Python  (data manipulation, automation, and modeling) and  SQL  (data querying, transformations).
  Data Analysis & Visualization:  Experience with  Power BI, Tableau, Matplotlib, or Seaborn  to create dashboards and analytical reports.
  Data Engineering:  Familiarity with  ETL processes, data pipelines, and data warehousing  solutions.
  AI & Machine Learning:  Knowledge of  generative AI, prompt engineering, and machine learning techniques  for energy market forecasting.
  Statistics & Forecasting:  Strong grasp of  statistical analysis, trend modeling, and forecasting methodologies.

Familiarity with  oil refining processes, fuel market economics, and industry regulations.
Understanding of  commodity markets, pricing mechanisms, and global energy trends.

  Strong analytical and problem-solving skills  to interpret large datasets and identify meaningful patterns.
  Excellent communication skills  to present complex data insights in a clear and impactful way.
Ability to  work independently , manage multiple projects, and meet deadlines in a fast-paced environment.
A  collaborative mindset  to engage with cross-functional teams and global experts in commodity markets.
  Attention to detail  and a commitment to maintaining data accuracy and integrity.

Experience working in a  refinery, chemical industry, or energy trading environment.
Hands-on experience with  machine learning deployment in a business setting.
Understanding of  big data technologies (Spark, Hadoop) and cloud-based data platforms (AWS, Azure, GCP).