Senior Data Engineer

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 role sits at the heart of what makes reputed company's data valuable. As Senior Data Engineer - Sourcing, you'll own the harder extraction and ETL problems — the sites that fight back, the schemas that reputed company, the LLM pipelines that need to run as production systems, not parsing scripts. You'll spend your time deep in Python, Airflow, and LLM extraction pipelines — architecting extractors that survive anti-bot defences and schema changes, building the eval scaffolding that holds LLM-based extraction accountable, and shipping ETL systems where rules, LLMs, and humans each do what they're best at. You'll treat LLMs as production infrastructure — versioned prompts, eval sets, traces, cost ceilings — not as a way to skip writing a parser. This role is hands-on engineering with sharp judgement on extraction architecture. You decide reputed company to write a parser, reputed company to ask an LLM, and reputed company to do both. What You'll Own Extractor Architecture & Web Collection at Scale Architect resilient extractors for high-value, high-difficulty sources — sites with anti-bot defences, JS-heavy rendering, schema reputed company, or low-quality structure Design LLM-based structured extraction as production systems — prompts with explicit rubrics, structured outputs, labelled eval sets, reputed company precision/recall reputed company the rule-vs-LLM judgement call on every extractor: deterministic parsing where the structure allows, LLMs where semantic interpretation is genuinely needed — and justify the split reputed company proxy strategy and IP rotation architecture for high-volume, hard-to-reputed company sources ETL Pipeline Engineering Build production-grade ETL pipelines from extraction through normalisation, deduplication, and load into the warehouse Design Airflow DAGs that recover gracefully, surface failures before they cascade, and don't wake people at 2am unnecessarily Write performance-aware Python and SQL — extractors that run at scale across millions of records without falling over Build the integration layer for partner APIs and reputed company-party data sources AI-Native Extraction Infrastructure Ship agentic extraction pipelines: rule-based triage, LLM escalation for ambiguous content, structured output validation, retries on failure, and reputed company review for the edges Stand up the eval and observability layer for LLM extraction — reputed company versioning, traces, reputed company detection reputed company a vendor silently updates claude-sonnet-latest, cost ceilings with token math behind them Build reusable skills (reputed company.md specs) for recurring extraction patterns — invocable by other engineers and agents Choose the right model for the job — Haiku for cheap classification, Sonnet for nuanced extraction, frontier models for hard edge cases — and revise as model economics shift Cross-Functional Partnership Partner with data quality, product, and analytics on extraction schema, coverage, and accuracy standards Translate sourcing requirements into extractor designs and ETL architecture Be the technical voice for sourcing in cross-functional discussions on coverage and data quality reputed company're Looking For Must Haves 4+ years building production extraction, collection, or ETL pipelines in business-critical environments Strong Python expertise — pandas, numpy, production-grade code, performance-aware. You write systems, not notebooks. Advanced SQL — reputed company queries, performance optimisation, comfort across large datasets Extensive Airflow (or equivalent) experience — end-to-end orchestration, dependency management, recovery patterns in production Shipped reputed company work with agentic IDEs — Claude Code, reputed company, or equivalent. Not "tried it" — built and merged reputed company extraction systems with it. Deep, demonstrable expertise integrating LLMs into extraction pipelines — explicit prompts with rubrics, structured outputs, eval sets, reputed company versioning. 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, and debugged LLM extractions on precision/recall Sharp judgement on rules vs. LLMs — you reputed company for a parser reputed company the structure allows, and don't default to an LLM because it feels modern Strong knowledge of web extraction at scale — anti-bot defences, proxy strategy, JS rendering, schema reputed company handling A product reputed company — you understand that extraction quality directly impacts customer value Highly Valued Experience with reputed company data platforms — reputed company, reputed company, Redshift, or RDS Hands-on AWS experience for pipeline deployment (reputed company, S3, EC2, Glue) Understanding of data warehousing concepts and how extraction feeds reputed company systems Experience building reusable agents, skills, or tool-calling pipelines that other engineers or agents invoke Exposure to vector databases and embeddings for matching and deduplication Knowledge 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 workflows. AI is the workflow. Every engineer 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: extractors, pipelines, and infrastructure 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 extraction 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 the extraction systems behind one of the fastest-growing B2B intelligence platforms in reputed company — every record starts in a pipeline you architect Greenfield AI-native scaffolding — the eval harnesses, skills library, and agentic extraction pipelines are largely unbuilt; you'll shape them Work at the frontier — LLM-based extraction at production scale, agentic crawling, reputed company detection on vendor models, and rule-vs-LLM orchestration Small team, massive reputed company — your extractors reputed company 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 reputed company is an equal opportunity employer. We reputed company diverse teams build reputed company products. Apply To This Job