Data Analyst
At a glance
Mid-level Data Analysis & BI role at Euromonitor. Shanghai · full-time.
Pay not stated Data Analyst salary
dataroles summary, based on the employer's posting.
What you'll do
- Clean and analyse structured and unstructured data with Excel, SQL and other relevant tools
- Use Python or R to automate analysis, validate data and build repeatable reporting workflows
- Enrich and check large datasets from web scraping, digital behaviour and other sources for client and internal work
- Build dashboards, visualisations, reports and presentations, then explain findings to technical and non-technical audiences
- Document methods and assumptions, maintain project files and support data governance and delivery standards
What you bring
- You can use SQL to extract, combine, filter and validate information from multiple sources
- You have working ability in Python or R for analysis, automation and reporting
- You have handled large, complex datasets, including web-scraped or digital behavioural data
- You can create dashboards and reports with tools such as Power BI or Tableau, and communicate useful business findings
- You understand data quality, governance, pipelines and ETL, and can apply sound coding and documentation practices
Who this fits
This mid-level role suits an analyst who can turn complex datasets into clear, dependable findings and work with global analyst, research, data and client-facing teams. The position is onsite in Shanghai. The scope of ownership varies with experience, from more guided work to leading complex workstreams and improving methods and processes.
From the employer
Description
This team is part of the Foundational Data function, where you will transform complex data and market information into clear, reliable, and actionable insights for clients worldwide. You will work with diverse data sources, support research and validation activities, and help create the strong data foundations needed for high-quality client solutions. You will join a collaborative, international team focused on learning, problem-solving and building scalable data capabilities. The level of responsibility will depend on experience, with earlier-level analysts receiving more guidance and more experienced analysts taking greater ownership of complex workstreams, methodology and process improvement.
Core Requirements:
- Extract, clean, organise, and analyse structured and unstructured data using Excel, SQL, and other relevant data tools.
- Use Python or R, where applicable, for data analysis, automation, validation, and repeatable reporting workflows.
- Work with large and complex datasets, including web-scraped, digital behavioural, website, database, and third-party source data.
- Effectively enrich, validate, and contextualise datasets for client and internal deliverables against available and trusted sources.
- Apply rigorous data quality, validation, and governance standards to ensure accuracy, consistency, and transparency of outputs.
- Create clear dashboards, visualisations, reports, and presentations using appropriate analytics and business intelligence tools.
- Interpret complex analytical findings and translate them into actionable insights for technical and non-technical audiences.
- Support data preparation, transformation, and governance activities aligned with project methods, frameworks, and delivery standards.
- Maintain organised project files, document assumptions and methodology, and use version control or structured documentation practices where required.
- Collaborate with global analyst, data, research, and client-facing teams to deliver high-quality output on time.
- Continuously build capability in data analytics, automation, visualisation, and emerging data science techniques as the role evolves.
- Leverage AI-powered tools and automation techniques to bring efficiency in all the above workflows.
Requirements
- Working knowledge of SQL for extracting, joining, filtering, and validating data from multiple sources.
- Working knowledge of Python or R for data analysis, automation, and reporting.
- Strong understanding of data analysis techniques, data validation, and data quality management.
- Experience working with large and complex datasets such as web scraped data or digital behavioural data.
- Ability to build meaningful visualizations, dashboards, and reports using industry-standard tools such as Power BI and Tableau.
- Strong analytical and problem-solving skills with the ability to derive actionable business insights from data.
- Ability to communicate complex analytical findings to both technical and non-technical audiences.
- Understanding of data structures, data pipelines, ETL processes, and data governance principles.
- Experience using version control tools (e.g., Git) and applying good coding and documentation practices.
- AI literacy, including effective use of AI tools, prompt engineering, automation, and agent-based workflows to enhance research, analysis, and operational efficiency.
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