Science and Technology Data Quality Analyst

Data Quality Analyst needed We have an opportunity for a client in the Flatiron district of NYC for a Data Quality Analyst. Ideally, this person will be local enough to go onsite for the first week and potential subsequent onsite visits. This is a
- 9 month contract but ultimately, they would like to convert to FTE. Therefore, this is a w2 contract only. Our client is building a best-in-class go-to-market engine, and clean, complete account and contact data is the foundation it runs on. As we scale across our primary markets, we're investing in a dedicated data quality function to keep that foundation accurate, complete, and enriched to the standard our sales motion requires. We're hiring a dedicated Data Quality Analyst to own this function. You'll sit inside our four walls, cognitively plugged into how we sell, living and breathing one thing: data quality. This is not a data-entry role. You'll work AI-first, using Clay, ZoomInfo, LinkedIn Sales Navigator, and AI agents to build a repeatable enrichment method once, then run it across tens of thousands of accounts. The goal is a small, highly leveraged function: figure out the “how” once, apply it at scale, and maximize the time our sales team spends selling. This work is the foundational: accurate, complete data is what continues to power our trajectory. Enrich accounts patch-by-patch, quality first: work patch of accounts at a time against our targeted buyer personas, prioritized by business impact, at a sustained pace of accounts enriched with validated contacts per day. – Build the repeatable method: design the enrichment workflow (sources, sequencing, validation, load) once, document it, and scale it, so enrichment runs as a repeatable system rather than a manual effort. – Own the intake queues: account-creation requests, the contact request queue, and the rep feedback loop (reps flag gap accounts and get validated contacts back within a week). – Run the vendor partnership desk: work ZoomInfo, Clay, and LinkedIn Sales Navigator daily, pressure-test each source (“I'm trying to find X, what's the best way?”), Act as the quality gate: validate and prep all data before it lands in Salesforce, within provenance and overwrite rules, and with opt-out and privacy checks enforced on every write. We ship quality, not junk. – Validate firmographics at scale: confirm our most important account signals/data across the seller books driving coverage toward an target on workable accounts and feeding real market sizing for planning. – Protect email deliverability: validate email accuracy before contacts enter outbound and marketing systems, protecting sender reputation and campaign performance. – Support buyer group completeness: as deal sizes grow, help ensure the full buyer group (not just the primary contact) is identified and covered on target accounts. – Maintain hygiene continuously: deduplication, normalization, and governed enrichment so data stays clean rather than being cleaned once. – Report progress daily against clear goals: accounts enriched per day, contact and persona coverage per patch, turnaround SLAs, and overall data reliability. 3+ years in data quality, data operations, RevOps, GTM operations, or a similar function where CRM data accuracy was your core responsibility. – Hands-on Salesforce data

experience:
deduplication, imports and mass updates, validation, data modeling basics, and reporting on data health. – Clay plus at least one major data provider (ZoomInfo, LinkedIn Sales Navigator, Cognism, Apollo, Clearbit, or similar). – Demonstrated AI fluency: you already use AI tools and agents (Claude, ChatGPT, Clay AI, or similar) to automate enrichment, QA, and research workflows, and you can point to concrete things you've shipped this way. – Strong data-profiling instincts: comfortable in spreadsheets and ideally SQL, able to spot bad data, diagnose why it's bad, and fix the process that produced it. – A self-directed, embedded working style: you sit close to sales, absorb how we sell, tolerate ambiguity, and turn messy asks into a documented, repeatable process. Bonus – Familiarity with legal-industry or professional-services data, e.g., understanding an in-house legal org (GC, Associate/Deputy GC, Legal Ops), legal team structures, or law firm vs. in-house distinctions. – Experience with data warehouses (Snowflake, BigQuery), iPaaS/API-based workflows, or scripting (Python) for data pipelines. – Prior success in a contract-to-hire or agency engagement building a data quality function from scratch. – Experience supporting outbound/ABM motions where persona-level contact accuracy directly drove pipeline. Contact coverage per patch: assigned account books fully covered with validated target-persona contacts, rolling up to the coverage goal. – Throughput: a sustained validated, persona-matched account enriched per day. – Turnaround SLAs: account-creation requests handled the same week; rep-flagged gap ac