Moore is looking to ensure data consistency, quality, and accessibility by supporting the execution and monitoring of global data integration processes and maintaining seamless data workflows.
Requirements
- Experience working with databases, ETL pipelines, or large-scale data systems.
- Foundational understanding of data processing, analytics, and data integration concepts.
- Proficiency in working with structured and semi-structured data formats.
- Familiarity with tools such as Excel, SQL, and data visualization software is preferred.
- Experience with version control tools like JIRA or GitHub is a plus.
- Basic knowledge of donor management systems or data services is advantageous.
- Strong troubleshooting skills to identify and resolve data-related issues.
Responsibilities
- Support the daily operation of data ingestion and transformation pipelines to ensure timely and accurate data flow.
- Monitor automated data jobs, troubleshoot errors, and escalate issues as necessary to maintain system stability.
- Validate data to ensure it aligns with established standards, resolving discrepancies between source data and integrated datasets.
- Conduct quality checks and validation procedures to maintain data accuracy, completeness, and consistency.
- Identify anomalies, missing information, or inconsistencies within datasets and coordinate resolution efforts with relevant teams.
- Maintain comprehensive records of data quality issues, resolutions, and ongoing monitoring activities.
- Document data integration processes, validation rules, and data standards to facilitate team understanding and compliance.
Other
- Highly organized, detail-oriented, and capable of prioritizing tasks effectively.
- Excellent verbal and written communication skills.
- Collaborate with data engineers, analysts, and other team members to optimize data workflows and improve processes.
- Support onboarding of new data sources, including testing and initial validation to ensure seamless integration.
- Identify opportunities to streamline existing data processes and enhance data quality through continuous improvement initiatives.
