Senior Data Management Professional - ESG Data ...
@ Bloomberg

Singapore, Singapore
SGD 120,000
On Site
Full Time
Posted 8 hours ago

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Job Details

Overview

Bloomberg runs on data. Our products are fueled by powerful information and intelligent context from around the world. As a Senior Data Management Professional, you will enhance ESG data quality for Bloomberg's Terminal and Enterprise products.

Role & Responsibilities

  • Define and execute a comprehensive strategy for ESG data quality.
  • Conduct data profiling and statistical analysis to ensure integrity.
  • Collaborate with cross-functional teams to implement scalable solutions.
  • Lead global initiatives and mentor junior colleagues.
  • Apply technical skills using Python, R, SQL and BI platforms.

Requirements

  • Excellent proficiency in written and spoken English and Korean.
  • A BA/BS degree in a related field with 4+ years experience.
  • Experience with data management, quality metrics and ETL processes.
  • Knowledge of sustainable finance, ESG disclosure frameworks and local reporting standards.

Our Team

You will be joining a department that powers trusted financial insights with global ESG data insights, working in a fast-paced, collaborative environment.

Key skills/competency

  • Data Quality
  • ESG Data
  • Data Management
  • Sustainable Finance
  • Statistical Analysis
  • Data Profiling
  • Python
  • SQL
  • ETL
  • Cross-functional Collaboration
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How to Get Hired at Bloomberg

🎯 Tips for Getting Hired

  • Customize your resume: Tailor skills to ESG and data quality.
  • Highlight industry expertise: Emphasize sustainable finance and technical abilities.
  • Research Bloomberg: Understand their data products and global impact.
  • Prepare examples: Demonstrate teamwork in multi-regional projects.

📝 Interview Preparation Advice

Technical Preparation

Review Python, R, SQL basics.
Practice data profiling techniques.
Study ETL and data modeling.
Learn BI tool dashboards.

Behavioral Questions

Describe teamwork in global projects.
Explain challenging data quality situations.
Discuss managing cross-functional priorities.
Share experiences mentoring team members.

Frequently Asked Questions