Monitor data quality, completeness, consistency, and accuracy across all enterprise systems on a continuous basis.
Maintain a data health scorecard by system and domain, updated on a defined cadence.
Identify anomalies, duplicates, orphaned records, broken mappings, and integration failures across CRM, AMS, ERP, data warehouse, and digital platforms.
Enforce data standards and governance policies set by the organization.
Flag and escalate non-compliant data, failed integrations, or system changes that violate established standards.
Maintain the enterprise system-of-record registry: for every data domain, document which system is authoritative, which systems consume the data, and which teams are accountable.
Track and publish the accountability matrix for data quality by system owner, updated as personnel or system scope changes.
Quantify system ownership gaps — domains without a name owner, systems without defined stewards, integration points without documented accountability.
Monitor integration health across iPaaS workflows, API connections, ETL processes, and data warehouse pipelines.
Validate that data written by integrations lands in the correct system of record, in the correct format, and at the expected frequency.
Identify and document integration failures, transformation errors, field mapping drift, and synchronization gaps.
Participate in integration design and implementation reviews to ensure governance standards are embedded before go-live, not retrofitted after.
Coordinate with integration partners and technical teams during incident resolution; the EIA owns the issue log and drives it to closure.
Review solution designs, data mapping documents, field definitions, and acceptance criteria for compliance with current governance standards.
Deliver a recurring data integrity status report summarizing system health, escalated issues, trends, and recommendations.
Translate technical data quality findings into plain-language summaries that system owners and business stakeholders can act on.
Requirements
Bachelor’s degree in information systems, business analytics, data management, computer science, or a related field.
Minimum 3 years of experience in data quality, data operations, systems administration, integration monitoring, or a closely related discipline.
Demonstrated experience monitoring and validating data across multiple enterprise systems simultaneously.
Working knowledge of CRM, AMS, ERP, data warehouse, and integration platform concepts and architectures.
Ability to read and interpret integration payloads, API documentation, ETL mapping documents, and data flow diagrams.
Experience producing data quality metrics, scorecards, or health reporting for operational and leadership audiences.
Clear written and verbal communication skills across technical, operational, and executive audiences.
Experience in HubSpot, Salesforce, Microsoft Dynamics or other cloud-based CRM administration and/or data management.
Familiarity with AMS platforms or comparable association management systems.
Exposure to data warehouse environments including Snowflake, Datadog, Databricks, Microsoft Fabric, Azure Synapse, or Power BI or other open-source semantic models.
Experience with iPaaS platforms such as MuleSoft, Boomi, Workato, n8n, Azure, or other open-source Integration Services.
Background in or exposure to data governance frameworks such as DAMA, DMBOK, DCAM or other data foundation and management layers — not as a designer but as an operator.
Experience working inside an active system implementation or enterprise architecture build-out.
Familiarity with SQL or equivalent query tools sufficient to validate data and investigate anomalies independently.
Exposure to Unix, Linux, or open-source tooling in a data operations or systems administration context.