Data Integrity Analyst
The Data Integrity Analyst plays a critical role in monitoring, auditing and improving data quality across the back-office data pipeline through mapping data flows, defining and enforcing data quality rules, collecting quality metrics through reports and dashboards, producing/monitoring data integrity scorecards, defining proposals to improve quality and ensuring proper implementation of data quality during the creation and modification of data. This role will initially focus on the lead to cash process but is expected to eventually focus across multiple back-office systems and perform related audits.
- Defines data quality rules, thresholds, and standard metrics/quality-expectations for data elements that support critical business processes and is an active advocate for recommendations on business process improvements.
- Builds and monitors data quality dashboards/scorecards on existing Salesforce data and performs ad hoc analysis of data quality issues.
- Proposes and implements industry best practices as it pertains to quality data.
- Works closely with business systems team on the development and implementation of the controls to mitigate data quality risk as required for accurate data migration and data modification
- Analyze, de-dupe, validate, and map data prior to data imports and updates.
- Partners cross-functionally with Finance, Marketing and Sales teams to establish metrics for data quality process assessment.
- Understand intricacies of system integrations as it relates to impacts on the data involved across multiple systems.
- Identify process gaps; actively proposes, advocates for, and implements remediation as required.
- Provide complete and scalable solutions to business data problems; performs root cause analysis of the data quality issues and drives corrective actions.
- Have a solid understanding of data lifecycle management across all systems functional areas/modules; this requires being well-versed in typical enterprise-level back office functional workflows (lead-to-opportunity-to-quote-to-order, etc.).
- Establish and enforce systems data retention policies, working with legal and data security teams.
- Exhibits diplomacy while establishing and maintaining cooperative working relationships with designated stakeholders.
- Proposes and performs data purging on regular basis as part of data lifecycle.
- Perform quarterly/annual audits of multiple internal systems across multiple data types
- Maintains & enforces the sanctions policy through audit as established by Sage
- Work with the legal department and perform data extracts as required.
- Meets & supports audits performed by external auditors by being designated contact and coordinator for data being audited.
- Ensures Salesforce data backup is performed on a regular basis.
- Bachelor's degree or equivalent work experience in Engineering, Computer Science, Business Information Systems
- 5+ years of industry experience focused on improving quality of the data in big data environments
- Passionate about data and improving data integrity across systems
- Detail oriented and proactive to identify, coordinate, and drive corrective actions
- Strong ability to apply analytic and critical thinking skills, writing skills, communication skills, consulting skills, and ability to work within a team
- Must be an independent self-starter with efficiency in managing time
- Ability to prioritize multiple tasks/projects in a rapidly changing environment
- Extensive familiarity with the Salesforce platform, Salesforce reporting and dashboard, data model, and data loading/manipulation tools; experience with DemandTools would be appreciated
- Experience building metadata repositories and data lineages using modern data discovery techniques and tools
- Advanced knowledge in Data Quality, Data Profiling and Data Integration tools and languages (e.g., SQL, Spark, Databricks, Snowflake, enterprise BI tools or other ETL and big data analysis software)
- Experience building executive-level dashboards using Tableau, Qlik Sense, or similar tools
- Demonstrates advanced skills in understanding and correcting data discrepancies, reading and translating data models, data querying, identifying data anomalies and performing root cause analysis
- Ability to work with data engineers and analysts to understand data requirements and translate them into data quality tasks
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