Data Quality Analyst (Salesforce Data Steward)

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.

• 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.

Preferred Qualifications

• 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.

Originally posted on Himalayas