- Manipulate and manage data from multiple sources into organised formats for analysis.
- Conduct quality assurance checks on survey data against questionnaires, scripts, routing logic, piping, quotas, sample definition, and other metrics affecting the completeness and accuracy of the data collection both within a project and across projects.
- Identify and report inconsistencies, missing data, extra data or other errors in data sets to the relevant teams, including the Project Manager.
- Perform data comparisons across relevant data sets to validate consistency and accuracy, detect trends or discrepancies, and work with relevant teams to resolve data quality issues.
- Standardise and clean data deliverables, including coding, formatting, labelling, and data maps in accordance with agreed specification and to support analysis purposes.
- Generate structured reports and summaries to support project insights and decision-making.
- Automate data processing tasks using Excel functions, macros, or scripts, where applicable.
- Support project management team by providing data extracts, summaries, or structured outputs as needed.
- Ensure compliance with data protection laws, privacy and security standards, including ISO 20252 and ISO 27001, following best practices in handling confidential information.
- Work closely with the project management team, survey programmers, survey testers, and other departments as required to clarify and implement adequate data requirements.
- Maintain or produce documentation on Company systems relating to relevant data checking, validation, and preparation processes, known issues and resolution, as relevant.
- Make recommendations to improve data checking, validation and preparation processes.
- 1-2 years of experience in market research is preferred but not required.
- At least 2 years of experience with Excel, including advanced Excel functions, tables, and data interpretation, with the ability to work with large datasets. An experience of SPSS will be a plus.
- Experience with data validation, cleaning, and processing techniques to ensure accuracy and consistency.
- Understanding of different data collection methodologies (e.g., telephone, social media and other network recruitment, panel management) would be a plus.
- Excellent knowledge of English (written and verbal) required.
- Strong time management and organizational skills.
- Ability to effectively prioritise tasks and manage multiple projects simultaneously.
- Excellent attention to detail with a methodical approach to problem-solving.
- A proactive attitude, with the ability to work independently and take initiative.
- Focused on delivering high-quality work without compromising deadlines.
- Experience in building and maintaining effective working relationships with internal teams and stakeholders.
- Ability to troubleshoot data-related issues and support teams in resolving data discrepancies.
- Awareness of data privacy and compliance regulations in market research.
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Data Quality Analyst - Manila - RONIN International
Paglalarawan
RONIN International (part of the Rippleffect Group) is an award-winning independent market research and consultancy company. Offering qualitative, quantitative, and mixed methods market research services globally, we are proud to be the trusted market research partner of many leading global brands. Our HQ is in London supported by local offices in Lancaster, Hamburg, New York, Hong Kong and our new office in Manila (Makati).
Position Summary:
The Data Quality Analyst is responsible for processing, structuring, and managing data from market research projects. This involves manipulating large datasets, running checks and validation to ensure data consistency and accuracy, and conducting quality checks to maintain data integrity.
The Data Quality Analyst will also perform data comparisons to identify inconsistencies, missing values, errors, and other data quality issues in project deliverables and perform root cause analysis to understand the source of the issue and recommend options for rectification.
Core duties:
Data checking and validation:
Data processing, deliverables and reporting:
Documentation and collaboration:
Experience and Knowledge:
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