Data Engineer

Qualitate•New York City, NY

16d•Onsite

About The Position

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 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 yours. 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.

Requirements

Strong in SQL. You can write complex, performant queries against relational datasets and understand how to reason about data at scale.
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.
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.
Comfortable working 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 will help maintain our TypeScript pipelines running on scheduled jobs and Postgres-native queues, S3 for object storage, and AWS Bedrock for retrieval.
Comfortable with ambiguity. Early-stage data infrastructure is messy — you're energized by building order out of that, not frustrated by it.

Nice To Haves

Experience with a modern warehouse and transformation stack (e.g. Snowflake/BigQuery, dbt, Airflow) is a plus.
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.

Responsibilities

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.