Data Quality Analyst (Salesforce Data Steward)

What you will do

• Data Quality & Stewardship
• Salesforce data quality — Manage and improve data quality across core objects (Accounts, Contacts, Opportunities), ensuring completeness and accuracy of key firmographic fields such as industry, company size, geography, and DUNS.
• Enrichment & standardization — Execute data enrichment activities using third-party providers and manual research; validate and standardize inbound data prior to updates.
• Cleansing at scale — Perform deduplication, cleansing, and bulk data updates using tools such as DemandTools and Data Loader.
• Reporting & monitoring — Develop data quality reports and dashboards (Power BI / Salesforce) to monitor KPIs, track data health, and identify trends.
• Governance & compliance — Enforce data governance standards, maintain documentation and metadata, and ensure compliance with privacy and regulatory requirements.
• AI & Automation for Data Validation
• Automated validation — Design, build, and maintain automated data-validation checks that continuously monitor Salesforce data against business rules, catching errors and gaps in near real time rather than after the fact.
• Unblock downstream processes — Ensure data quality issues do not block or delay critical downstream revenue processes (Quote-to-Cash). Proactively detect, flag, and resolve records that would otherwise fail these processes, and build alerting so problems are caught before they stall the business.
• Apply AI / LLMs — Use AI and large language models to accelerate data validation, matching, classification, enrichment, and anomaly detection — for example, standardizing messy inbound data, identifying likely duplicates, or explaining why a record failed a rule.
• Build automation — Develop and maintain SQL / Python-based data processing, validation pipelines, and automation; reduce manual data work through repeatable, self-service, and scheduled processes.
• Partner with technical teams — Collaborate with technical and RevOps teams on integrations, workflows, and system design so that data quality is enforced upstream, at the point of entry, wherever possible.
• Measure & improve — Track the impact of automation on data quality and process throughput, and continuously expand coverage of validated fields, objects, and business rules.