Data Transformation Engineer

The Team:

The Forecast Ops team within the Analytics Enablement group drives innovation and efficiency gains for economic forecasting processes. The team creates data pipelines, designs automated processes, and assists forecast product teams with improving their procedures.

Responsibilities and Impact:

We are seeking a talented and motivated Data Transformation Engineer to join our team. The primary responsibility of this role is to develop and maintain scripts in Python and EViews to clean and enrich time-series data. The ideal candidate will have a strong understanding of time-series data and possess excellent technical skills to automate data processing workflows efficiently.

• Develop and maintain Python and EViews scripts to clean, enrich, and transform time-series data related to economic and industry information.
• Design and implement automated data pipelines to ensure the efficient processing and integration of large datasets.
• Collaborate with data analysts, economists, and other stakeholders to understand data requirements and deliver accurate and timely data solutions.
• Monitor and optimize the performance of data pipelines, ensuring data integrity and quality.
• Troubleshoot and resolve data-related issues, implementing improvements to enhance data processing efficiency.
• Document processes, methodologies, and best practices for data transformation and automation.
• Stay up-to-date with industry trends and advancements in data engineering and automation technologies.

What We’re Looking For:

Basic Required Qualifications:

• Bachelor’s degree in Computer Science, Data Science, Economics, or a related field.
• Strong proficiency in software used for data processing and automation, preferably Python and EViews.
• Solid understanding of time-series data and related statistical techniques.
• Experience with data cleaning, transformation, and enrichment processes.
• Excellent problem-solving skills and attention to detail.
• Ability to work independently and collaboratively in a fast-paced environment.
• Strong communication skills to effectively interact with team members and stakeholders.

Additional Preferred Qualifications:

• Experience with other data processing and automation tools and languages, such as R, or other statistical software commonly used for economic time-series analysis, such as SPSS, SAS, or MATLAB.
• Familiarity with economic and industry time-series data.
• Knowledge of data visualization techniques and tools.
• Understanding of database management systems and SQL.

About S&P Global Market Intelligence

At S&P Global Market Intelligence, a division of S&P Global we understand the importance of accurate, deep and insightful information. Our team of experts delivers unrivaled insights and leading data and technology solutions, partnering with customers to expand their perspective, operate with confidence, and make decisions with conviction.