Data Quality Analyst (Salesforce Data Steward)
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 2d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 35d ago
The date the source published, not the day we noticed it (2026-08-11). Last seen at its source just now.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Philippines
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay A$1,675–2,250/mo
Middle 50% of 19 listings that do state pay — Operations · all levels · Philippines · AUD/month. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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lever employer's own board first seen 26d ago · last seen just now
The listing
Role Summary
What you will do
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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.
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Enrichment & standardization — Execute data enrichment activities using third-party providers and manual research; validate and standardize inbound data prior to updates.
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Cleansing at scale — Perform deduplication, cleansing, and bulk data updates using tools such as DemandTools and Data Loader.
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Reporting & monitoring — Develop data quality reports and dashboards (Power BI / Salesforce) to monitor KPIs, track data health, and identify trends.
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Governance & compliance — Enforce data governance standards, maintain documentation and metadata, and ensure compliance with privacy and regulatory requirements.
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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.
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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.
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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.
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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.
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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.
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Measure & improve — Track the impact of automation on data quality and process throughput, and continuously expand coverage of validated fields, objects, and business rules.
Data Quality & Stewardship
AI & Automation for Data Validation
What you will bring
- 4+ years of strong, hands-on experience with Salesforce (SFDC) data management and data models.
- Proficiency in SQL and Python for data analysis and automation.
- Hands-on experience building automated data validations or data-quality checks, and comfort translating business rules into automated logic.
- Experience applying AI/automation to data work (e.g., AI/LLM-assisted matching, classification, enrichment, or anomaly detection), or a demonstrated aptitude and eagerness to do so.
- Hands-on experience with DemandTools or similar data transformation/deduplication tools.
- Experience with Power BI or the Power Platform; intermediate to advanced Excel skills.
- Proven background in data cleansing, deduplication, and large-scale data management.
- Strong analytical skills, attention to detail, and bias toward automating repetitive work.
- Understanding of Quote-to-Cash / order management processes (e.g., quoting and approvals, ordering, provisioning, and billing) and how data quality affects them.
- Experience with Dun & Bradstreet (D&B) or other enrichment providers.
- Experience with Salesforce APIs / SOQL and building integrations or workflow automation.
- Experience with Snowflake or other cloud data platforms.
- Familiarity with data governance frameworks and master data management concepts.
- Experience with AI/LLM tooling or frameworks applied to data quality or process automation.
- Salesforce certifications a plus.
Preferred Qualifications