Data Engineer

We need to output HTML fragment with sections: About the role? Actually the posting includes sections: About Qualitate, The Role, What You’ll Own, Who This Role Is For, What Sets You Apart, Compensation & Benefits, What to Expect. We need to use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). The instruction: "use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." So we should map sections accordingly. We need to not repeat job title as heading. The posting doesn't have explicit job title but likely "Data Engineer". We should not repeat it. We need to include sections only when posting has real specific content. We have content for About Qualitate (company description). That could be "About the role"? Actually "About Qualitate" is about company. Could map to "About the role"? The instruction says use headings like About the role, Responsibilities, etc. But we can use any heading that matches content. Probably we should use headings: About Qualitate (maybe as About the role?), Responsibilities (What You’ll Own), Requirements (Who This Role Is For + What Sets You Apart), Qualifications (maybe not separate), Skills (maybe not), Benefits (Compensation & Benefits), Pay (maybe part of Benefits), Schedule (Location). We need to decide. We must not create sections with filler like "not specified". We have specific content for compensation, benefits, location. So we can have sections: About Qualitate, Responsibilities, Requirements, Benefits, Pay, Schedule? But Pay and Benefits may overlap. The instruction says prefer a few substantive sections over many thin ones. So we could combine Pay and Benefits into one Benefits section. Schedule maybe location. Thus sections: About Qualitate (company overview), Responsibilities (What You’ll Own), Requirements (Who This Role Is For + What Sets You Apart), Benefits (Compensation & Benefits), Schedule (Location). Also maybe "What to Expect" could be part of About Qualitate? But it's about culture; could be included in About Qualitate or separate. But we prefer few sections; maybe incorporate into About Qualitate. We must use

• for any list of duties/requirements/benefits. So we need to turn bullet points into lists. We must preserve all substantive information, not invent or summarize away detail. So we need to include all details from each section. Let's extract content: About Qualitate paragraph: "Qualitate is building the AI-native continuous intelligence platform for enterprises, investment firms, and asset managers. We automate the entire primary research workflow, from study design and expert recruitment through AI-moderated interviews and insight synthesis, replacing weeks of manual research with high output signal in hours. Primary research remains slow and transactional for everyone involved. Investment and corporate strategy teams spend hundreds of hours coordinating intermediaries, scheduling calls, and synthesizing fragmented insights. Experts face repetitive outreach, screener calls, schedule wrangling, and one-off engagements that offer them little control or lasting value. Qualitate transforms this experience with an AI-native expert network platform that gives participants the flexibility to contribute on their own time while enabling research at unprecedented scale. Qualitate’s AI Moderator conducts thousands of conversations with experts each month, capturing forward-looking purchasing plans, competitive displacement, and real-time ROI signals. Our continuous intelligence platform then turns these conversations into quantified, time-series outputs tracking over 10,000 public and private companies, queryable instantly in natural language. Our customers include some of the world's largest enterprises, hedge funds, PE firms, credit investors, and venture capital firms." The Role paragraph: "Qualitate is building the primary research layer for investors and corporate strategy teams. Our AI interviews experts, turns those conversations into structured intelligence, and delivers it to the funds and companies acting on it. Next, we're turning that into an always-on intelligence platform. Customers will bring their own proprietary data alongside ours, and AI agents will run research workflows end to end. All of that runs on data, and as our first dedicated Data Engineer, you'll own it. You'll build the pipelines that feed our AI moderator, the models that turn thousands of expert conversations into structured, searchable insights, and the systems that will let customers plug their own data into Qualitate. You'll work closely with Applied AI, Market Research, and Engineering, but the architecture decisions are ours. Our clients make investment decisions on this data, so accuracy isn't a feature. It's the product. You'll set that bar and shape how data works at Qualitate for years to come." What You’ll Own bullet list (multiple sentences each starting with a bold phrase). Let's list: - Build and maintain data pipelines. Design ETL/ELT processes that ingest unstructured data, quantitative metrics, 3rd party datasets into our core systems. - Design data models and data systems. Build and evolve data models across relational and non-relational systems. You'll work with structured application data in relational databases alongside unstructured data stored in S3 and indexed in semantic search engines. - Build AI-powered data enrichment pipelines. Use LLMs and other AI systems to extract structured information from unstructured data, classify and enrich records, and integrate AI-generated outputs into reliable production data workflows and evaluation systems. - Own data quality end-to-end. Build automated QA checks, monitoring, and alerting to catch anomalies before they become client-facing problems. - Partner cross-functionally. Work directly with Research, Applied AI, and Engineering to define schemas, data contracts, and reporting needs as new features are shipped. - Document as you build. Maintain clear data lineage, schema documentation, and runbooks — you're building the systems, so you're setting the data foundation of the company. Who This Role Is For bullet list: - You're strong in SQL. You can write complex, performant queries against relational datasets and understand how to reason about data at scale. - You're a capable software engineer. You have experience with either TypeScript or Python and can write production-quality code for automation, data pipelines, integrations, and internal tooling. - You can build robust data integrations. You can work with APIs, databases, files, and other external data sources and build reliable ingestion processes around authentication, pagination, rate limits, incremental extraction, retries, schema changes, and error handling. - Research and integrate third-party datasets. Research and evaluate company identifiers, entity-matching strategies, corporate hierarchies, subsidiaries, acquisitions, and build reliable processes to map and reconcile these entities across different data sources. - You can work across different types of data. You're comfortable with structured, semi-structured, and unstructured data and understand the tradeoffs between relational databases, object storage, search/indexing systems, and analytical data stores. - You'll work with our current stack and help shape what comes next. You will help maintain our TypeScript pipelines running on scheduled jobs and Postgres-native queues, S3 for object storage, and AWS Bedrock for retrieval. Experience with a modern warehouse and transformation stack (e.g. Snowflake/BigQuery, dbt, Airflow) is a plus. - You're comfortable with ambiguity. Early-stage data infrastructure is messy — you're energized by building order out of that, not frustrated by it. What Sets You Apart bullet list: - Prior experience in data engineering for a technology startup, ideally in fintech - You've been early on the engineering team at a company and helped build the function from scratch — not just used tools others built. - Exposure to LLM-driven data pipelines (labeling, moderation, quality scoring) is a strong plus. - You've worked in market research, expert networks, or financial services and understand the data integrity bar those industries demand. Compensation & Benefits paragraph: "Base salary: $170-200K depending on experience. Bonus: Eligible for cash bonus tied to individual and company performance Equity: Meaningful early-stage grant with upside tied to company growth. Benefits: Health coverage, flexible PTO, paid holidays. Location: Full-time, in-person in New York City." What to Expect paragraph: list of bullet-like sentences: - Excellence. You'll be held accountable to an exceptionally high bar for quality and impact. - High velocity. Fast-paced work environment in terms of growth, decision making, and time to impact. - High agency. Ownership over how you work, not just what you work on. We hire people we trust to figure it out. - Shared ambition. A small, early team disrupting the multi-billion dollar market research industry with AI. - Joy. We work hard and genuinely enjoy working with each other. Dedication to our work is matched only by our passion for fostering an enjoyable, collaborative work environment. We need to decide sections. Potential sections:

  About Qualitate

  (company description + The Role? maybe combine). But The Role is also substantive. Could separate: About Qualitate (company overview) and The Role (role description). However instruction says use headings like About the role, Responsibilities, etc. But we can use About Qualitate as heading? It says "e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule." It's examples, not strict list. So we can use About Qualitate. We need to avoid duplicate content. The Role paragraph is distinct from About Qualitate. Could make sections: About Qualitate, Responsibilities (What You’ll Own), Requirements (Who This Role Is For + What Sets You Apart), Benefits (Compensation & Benefits), Schedule (Location), Culture (What to Expect). That's many sections but each substantive. But instruction: "Prefer a few substantive sections over many thin ones." So maybe combine Requirements and What Sets You Apart into one Requirements section. Combine Benefits and Pay into Benefits. Combine Schedule and Location maybe into Schedule. Combine What to Expect maybe into About Qualitate or separate Culture. Let's aim for 4 sections: About Qualitate (company + role overview?), Responsibilities, Requirements, Benefits & Schedule (combined). But we need to preserve all detail; combining may lose clarity but still preserve. Better: About Qualitate (company description), Responsibilities (What You’ll Own), Requirements (Who This Role Is For + What Sets You Apart), Benefits & Schedule (Compensation & Benefits + Location + What to Expect). That's 4 sections. Let's craft. Section About Qualitate: include About Qualitate paragraph + The Role paragraph? The Role paragraph is about the role, not company. But we could include it under About Qualitate as role overview. Might be okay. But we need to not repeat job title as heading. We'll not. Now produce HTML. We'll use

  for each section heading. Inside each section, we will have

  for prose, and

  • for lists. We need to ensure we don't include boilerplate like EEO etc. Not present. Let's draft. About Qualitate section:

    Qualitate is building the AI-native continuous intelligence platform for enterprises, investment firms, and asset managers. We automate the entire primary research workflow, from study design and expert recruitment through AI-moderated interviews and insight synthesis, replacing weeks of manual research with high output signal in hours. Primary research remains slow and transactional for everyone involved. Investment and corporate strategy teams spend hundreds of hours coordinating intermediaries, scheduling calls, and synthesizing fragmented insights. Experts face repetitive outreach, screener calls, schedule wrangling, and one-off engagements that offer them little control or lasting value. Qualitate transforms this experience with an AI-native expert network platform that gives participants the flexibility to contribute on their own time while enabling research at unprecedented scale. Qualitate’s AI Moderator conducts thousands of conversations with experts each month, capturing forward-looking purchasing plans, competitive displacement, and real-time ROI signals. Our continuous intelligence platform then turns these conversations into quantified, time-series outputs tracking over 10,000 public and private companies, queryable instantly in natural language. Our customers include some of the world's largest enterprises, hedge funds, PE firms, credit investors, and venture capital firms.

    Qualitate is building the primary research layer for investors and corporate strategy teams. Our AI interviews experts, turns those conversations into structured intelligence, and delivers it to the funds