Data Quality Analyst (Salesforce Data Steward)

Philippines / India Finance 6 Financial Business Systems and Operations / Remote

Role Summary

We are seeking a Data Quality Analyst (Salesforce Data Steward) to ensure the accuracy, completeness, and integrity of customer and account data within Salesforce. This role supports global Sales, Finance, and Operations teams by maintaining high-quality data standards, enabling reporting, and driving scalable, automated data processes. Beyond traditional stewardship, this role is expected to leverage AI and automation to proactively validate data, surface and resolve exceptions before they cause problems, and ensure that data issues never block critical downstream revenue processes (Quote-to-Cash). The ideal candidate pairs deep Salesforce data expertise with a builder's mindset 6 someone who would rather automate a recurring validation than run it by hand every week.

What you will do

Data Quality & Stewardship

• Salesforce data quality 6 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 6 Execute data enrichment activities using third-party providers and manual research; validate and standardize inbound data prior to updates.
• Cleansing at scale 6 Perform deduplication, cleansing, and bulk data updates using tools such as DemandTools and Data Loader.
• Reporting & monitoring 6 Develop data quality reports and dashboards (Power BI / Salesforce) to monitor KPIs, track data health, and identify trends.
• Governance & compliance 6 Enforce data governance standards, maintain documentation and metadata, and ensure compliance with privacy and regulatory requirements.

AI & Automation for Data Validation

• Automated validation 6 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 6 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 6 Use AI and large language models to accelerate data validation, matching, classification,...