Data Engineer (AI)

Location: New York City; full-time, in-person

Base salary: $170,000–$200,000 per year, depending on experience. Additional compensation includes a performance-based cash bonus and equity.

Company

hirify.global is building an AI-native continuous intelligence platform that transforms primary research and expert conversations into structured, searchable intelligence for enterprises, investment firms, and asset managers.

What you will do

• Design, build, and maintain ETL/ELT pipelines ingesting unstructured data, quantitative metrics, and third-party datasets.
• Build and evolve data models across relational and non-relational systems, including Postgres, S3, and semantic search systems.
• Develop AI-powered enrichment workflows using LLMs to extract, classify, and enrich information from unstructured data.
• Own data quality through automated checks, monitoring, alerting, evaluation systems, and reliable production workflows.
• Build integrations with APIs, databases, files, and external data sources while handling authentication, pagination, rate limits, retries, and schema changes.
• Partner with Research, Applied AI, and Engineering to define schemas, data contracts, reporting requirements, documentation, data lineage, and runbooks.

Requirements

• Strong SQL skills and the ability to write complex, performant queries against relational datasets.
• Production software engineering experience with TypeScript or Python.
• Experience building reliable data integrations and ingestion processes.
• Comfort working with structured, semi-structured, and unstructured data across relational databases, object storage, search systems, and analytical data stores.
• Experience with TypeScript pipelines, scheduled jobs, Postgres-native queues, S3, and AWS Bedrock is relevant to the current stack.
• Ability to work full-time in person in New York City.

Nice to have

• Experience with Snowflake or BigQuery, dbt, or Airflow.
• Data engineering experience at a technology startup, ideally in fintech.
• Experience building an engineering function or data capability from scratch.
• Exposure to LLM-driven data pipelines, including labeling, moderation, and quality scoring.
• Experience in market research, expert networks, or financial services.

Culture & Benefits

• Early-stage environment with high ownership and agency.
• Fast decision-making and a high bar for quality and impact.
• Health coverage, flexible PTO, and paid holidays.
• Cash bonus tied to individual and company performance.
• Meaningful early-stage equity grant tied to company growth.
• Collaborative environment focused on ambitious work and enjoyment.