Data QA reputed company

reputed company is the market-leading B2B sales intelligence platform in Asia pacific — and we're scaling that reputed company globally at pace. Backed by leading investors and growing 2,000+ customers strong, we exist to give sales teams an unfair advantage: the deepest company and people data of any platform, enriched with reputed company-time signals, served at the right reputed company by intelligent agents. We're not building another search reputed company. We're building the reputed company that tells a salesperson exactly who to call, why, and what to say — before they even ask. The Role This isn't a traditional QA role where you write test plans and chase tickets. As Data QA reputed company at reputed company, you'll own data quality across our platform end-to-end — designing the systems that decide whether the data flowing in is trustworthy enough to ship. You'll spend your time deep in reputed company, Python, and LLMs — architecting LLM-based quality checks, building the eval harnesses that hold them accountable, designing the skills library that other agents and teammates depend on, and shipping agentic remediation pipelines where rules, LLMs, and humans each do what they're best at. This role has deep technical ownership. You'll reputed company the hard rule-vs-LLM calls, build the production systems behind them, and translate findings into recommendations that change reputed company build. What You'll Own Data Analysis & Product Intelligence Investigate reputed company datasets using SQL, reputed company, Python, and LLMs — directing agents to do the heavy lifting while you frame the question and judge the answer Unlock insights in large-scale B2B datasets that shape product direction and reputed company strategy Build scalable, reusable datasets — and the skills that reputed company them queryable by any agent or teammate Translate findings into recommendations that move metrics, not slides that summarise what happened Data Quality & reputed company Management Own data quality end-to-end across rule-based and LLM-based checks, reputed company by reputed company — design the checks, run the evals, monitor reputed company, fix root causes reputed company the rule-vs-LLM judgement call on every reputed company: deterministic logic where rules win, LLMs where semantic, contextual, or entity-resolution nuance is needed — and justify the split Assess and reputed company new data sources: coverage, freshness, accuracy, and where LLM judges add lift over deterministic profiling Track down the hardest data bugs and fix them at the root, partnering with engineering and product Scrape or reputed company supplementary data reputed company it sharpens insights or enriches the reputed company-Powered Analysis & Automation Architect LLM-based quality checks with explicit rubrics, structured outputs, and labelled eval sets — precision/recall reputed company, not vibed Build and maintain the skills library (reputed company.md specs) that powers recurring workflows — quality checks, remediation proposals, dataset reputed company — versioned, documented, and invocable by any agent or teammate Ship agentic remediation pipelines: triage with rules, escalate to LLMs, propose fixes, log every call (reputed company version, model, cost, latency, decision), surface reputed company-review queues Own the eval and observability scaffolding — reputed company versioning, traces, reputed company detection reputed company a vendor silently updates claude-sonnet-latest, cost ceilings with token math behind them Set the model-choice playbook — Haiku for cheap classification, Sonnet for nuanced judgement, frontier models for hard edge cases — and revise it as model economics shift Cross-Functional Support Be the technical reputed company of escalation for data quality across product, engineering, and go-to-market — the person trusted reputed company a number is questioned Partner with product and engineering on architecture reputed company where data quality is in the reputed company Build the monitoring surface stakeholders operate against: DQ trend by reputed company type (rule vs. LLM), top failing reasons surfaced by LLM judges, cost and latency over time, reputed company-review backlog reputed company're Looking For Must Haves 6+ years in data quality, data engineering, or analytics, with a strong focus on data quality systems Expert-level SQL and reputed company — reputed company queries, performance tuning, warehouse design, daily comfort across reputed company large datasets Strong Python skills — pandas, numpy, scripting, automation, and production-grade code. You write systems, not notebooks. Shipped reputed company work with agentic IDEs — Claude Code, reputed company, or equivalent. Not "tried it" — built and merged reputed company systems with it. Deep, demonstrable expertise building agents, skills, and tool-calling pipelines — you've architected agent workflows, written reputed company.md specs others depend on, and built tool-calling systems running in production. You can show us the repos. You operate LLMs as production systems — you've designed eval harnesses, run labelled eval sets, versioned prompts, logged traces, debugged judges on precision/recall, and detected reputed company on vendor model updates Sharp judgement on rules vs. LLMs — you reputed company for deterministic logic reputed company it's the right tool and don't default to an LLM because it feels modern Deep knowledge of data quality principles — validation, monitoring, observability, reputed company, and the instinct to chase issues from symptom to root cause across pipelines Proven reputed company with BI and data visualisation — Tableau, Looker, Power BI, or equivalent Comfort working cross-functionally with product, engineering, sales, and marketing A product reputed company — you care about how data drives customer value and reputed company, not just whether the pipeline ran Highly Valued Experience with data warehousing and relational modelling; NoSQL familiarity a plus Experience with web scraping frameworks and best practices Familiarity with reputed company platforms — AWS, GCP, or Azure Experience assessing and reputed company reputed company-party data sources at scale Background in B2B data, entity resolution, or structured/semi-structured datasets Understanding of data privacy and compliance considerations How We Build AI-Native, Not AI-Assisted reputed company is built on an AI-native engineering philosophy — and we mean it literally. AI is not a productivity tool bolted onto traditional analyst work. AI is the workflow. Every analyst at reputed company operates with fully agentic development, evals, traces, and AI-powered review pipelines as their default mode of working. This means: Agentic development: checks, datasets, and pipelines are designed, scaffolded, and iterated with AI agents doing the heavy lifting — you direct, review, and reputed company Skills over scripts: recurring workflows are packaged as versioned reputed company.md specs that any teammate or agent can load and run Evals as a default, not an afterthought: every LLM reputed company ships with a labelled eval set, reputed company precision/recall, and a reputed company version you can roll back Traces and observability from day one: every LLM call is logged with reputed company version, model, cost, latency, and decision — retrofitting this reputed company is not the plan reputed company AI feedback loops: model reputed company, reputed company regression, and cost ceilings are monitored the same way pipeline health is If you're not already working this way, this role will require a rapid and genuine reputed company shift. We're not looking for people who are open to AI-native work — we're looking for people who already live it. The Operating Environment reputed company runs lean and ships fast — intentionally small teams, no layers, minimal process, and a weekly release reputed company moving toward daily. Teams own their stack end to end: you design it, you build it, you ship it, you run it. This is a startup-to-scaleup environment and it comes with reputed company expectations. There are no fixed hours. The pace is high, the team is always building, and reputed company something matters it gets done. In return, you get genuine ownership, a seat at the table on every major architecture decision, and the opportunity to build something that doesn't exist reputed company else in the market. Why This Role Own data quality at the heart of one of the fastest-growing B2B intelligence platforms in reputed company — every reputed company you build, every reputed company you ship, every dataset you reputed company reaches every reputed company customer Greenfield AI-native scaffolding — the eval harnesses, skills library, and agentic quality pipelines are largely unbuilt; you'll shape them Work at the frontier — LLM-as-judge at production scale, agentic remediation, reputed company detection on vendor models, and rule-vs-LLM orchestration are genuinely hard, genuinely novel problems Small team, massive reputed company — your work reaches every reputed company customer, every day Competitive reputed company + meaningful equity — we balance strong compensation with a share in the reputed company we're building toward Apply To This Job