Data Engineer

Description

The company is an AI-native continuous intelligence platform for enterprises, investment firms and asset managers. It runs the whole primary research process: study design, expert recruiting, AI-moderated interviews and insight synthesis. The aim is to turn weeks of manual research into usable signal within hours.

Primary research is slow and costly for both sides. Strategy teams at investment firms and corporations lose hundreds of hours to intermediaries, call scheduling and merging scattered findings. Experts get repeated outreach, screening calls and one-off engagements that give them little say and little lasting value.

The company runs an expert network on AI. Participants choose when they take part. Its AI Moderator holds thousands of expert conversations a month, capturing purchase plans, competitive switching and real-time ROI signals. The platform turns these into time-series outputs covering more than 10,000 public and private companies, which can be queried in natural language. Customers include some of the largest enterprises in the world, hedge funds, PE firms, credit investors and venture capital firms.

The company is building the primary research layer for investors and strategy teams, and next an always-on intelligence platform where customers bring their own proprietary data and AI agents run research workflows end to end.

This is the first dedicated Data Engineer role. Clients make investment decisions based on this data, so accuracy is central to the product. You will work with Applied AI, Market Research and Engineering, and you will make the architecture calls.

Responsibilities

• Design and maintain ETL and ELT pipelines that bring unstructured data, quantitative metrics and third-party datasets into the core systems.
• Build and evolve data models across relational and non-relational stores, covering structured application data in relational databases and unstructured data in S3 indexed by semantic search engines.
• Create AI-driven enrichment pipelines that use LLMs and other AI systems to pull structured fields from unstructured data, classify and enrich records, and feed AI outputs into dependable production workflows and evaluation systems.
• Own data quality from start to finish with automated checks, monitoring and alerts that catch anomalies before clients see them.
• Work with Research, Applied AI and Engineering to set schemas, data contracts and reporting needs as new features ship.
• Keep data lineage, schema documentation and runbooks current as you build, since you are laying the data foundation for the company.

Requirements

• Strong SQL skills, including complex, high-performance queries on relational data at scale.
• Production-quality software engineering in TypeScript or Python for automation, pipelines, integrations and internal tools.
• Experience building reliable ingestion from APIs, databases, files and other external sources, handling authentication, pagination, rate limits, incremental extraction, retries, schema changes and errors.
• Experience researching and evaluating company identifiers, entity matching, corporate hierarchies, subsidiaries and acquisitions, and reconciling entities across sources.
• Comfort with structured, semi-structured and unstructured data, and an understanding of the tradeoffs between relational databases, object storage, search and indexing systems, and analytical stores.
• You will maintain the current TypeScript pipelines, which run on scheduled jobs and Postgres-native queues, along with S3 object storage and AWS Bedrock retrieval, and help choose what comes next. Experience with a modern warehouse and transformation stack such as Snowflake, BigQuery, dbt or Airflow is a plus.
• Comfort working in unclear conditions. Early-stage data infrastructure is messy, and you enjoy turning that into order.
• Nice to have: prior data engineering experience at a technology startup, ideally in fintech.
• Nice to have: experience being early on an engineering team and building its data function from scratch, not just using tools others built.
• Nice to have: exposure to LLM-driven pipelines for labeling, moderation or quality scoring.
• Nice to have: experience in market research, expert networks or financial services, and an understanding of the data integrity standards those industries require.

Conditions

Base salary of $170-200K, depending on experience.

Eligible for a cash bonus based on individual and company performance.

Equity: a meaningful early-stage grant that grows with the company.

The team is small and early, with a high bar for quality and impact, a fast pace across growth, decisions and delivery, and high personal ownership over how work gets done.

Equal opportunity statement: applicants are considered without regard to protected characteristics.

Benefits

• Equity grant tied to company growth.
• Cash bonus tied to individual and company performance.
• A collaborative team that enjoys working together.