Senior Data Analyst

at  NTT Data

SUMMARY

You will play a critical role in driving informed decision-making processes by analyzing complex data sets and transforming raw data into actionable insights. The ideal candidate should possess a strong analytical mindset, exceptional problem-solving capabilities, and a solid understanding of financial metrics. Your responsibilities will include developing and maintaining dashboards, conducting in-depth market research, performing trend analysis, and collaborating with cross-functional teams to ensure data integrity. This role requires a deep-seated proficiency in data visualization tools and statistical analysis software, as well as a robust ability to communicate findings effectively to stakeholders.

REQUIREMENTS

• Bachelor’s degree in computer science, Information Technology, or a related field; advanced degree preferred.
• Minimum of 8+ years of experience in data engineering or a similar role.
• Experience in the Pydata stack: Pandas, Numpy. And Knowledge of data modelling in Python and Excel
• Intermediate Excel skills: countifs, sumifs, pivot table.
• Strong proficiency in statistical analysis software and data visualization tools such as Tableau, Power BI, or similar.
• Knowledge of relational database technologies such as Teradata, Oracle and Microsoft SQL with strong SQL execution and optimisation skills.
• Excellent documentation, report writing & presentation skills.

• Work with Data scientists/Business Analysts/Product Owner to understand MI and dashboard requirements.
• Design Data Solutions: Leverage your analytical skills to design innovative data solutions that address complex business requirements and drive decision-making.
• Create detailed field level mappings as needed for different MI Dashboards.
• Develop and maintain comprehensive dashboards to visualize key performance indicators.
• Conduct market research and trend analysis to inform strategic business decisions.
• Collaborate with cross-functional teams to ensure data accuracy and integrity.
• Identify and recommend process improvements based on data analysis findings.
• Prepare detailed reports and presentations on analytical findings for stakeholders.
• Stay updated on industry trends, data analysis techniques, and best practices.