Data Engineer, AI & Data Infrastructure

Our client is an early-stage AI software company helping organizations turn complex information into timely, actionable insights. The product brings together information from multiple sources, including structured records and unstructured content, and places a premium on data accuracy and reliability.

The team is growing its engineering capabilities and is looking for a data engineer who wants to build foundational systems, collaborate closely with AI and product engineers, and influence technical decisions as the platform scales.

As a Data Engineer, you will own a broad portion of the data foundation behind an AI-enabled software platform. This is a hands-on role with meaningful architectural ownership, from reliable source integrations to the data models and quality controls used by product and engineering teams.

What you’ll do

• Design, build, and maintain ETL/ELT pipelines that bring application data, external datasets, metrics, and unstructured content into production systems.
• Create and evolve models across relational databases, object storage, and search or indexing systems.
• Build AI-assisted enrichment workflows that classify information, extract structured attributes, reconcile records, and assess output quality.
• Develop automated data validation, anomaly detection, monitoring, and alerting so issues are caught early.
• Integrate external APIs and data providers, including secure authentication, pagination, incremental synchronization, retries, and changing schemas.
• Work with AI, engineering, and other business teams to define data contracts, schemas, and requirements for new features.
• Establish maintainable documentation, data lineage, and operating procedures for the pipelines you build.

You will have room to select appropriate tools and improve the architecture rather than simply operate an established data stack.

• Strong SQL skills, including complex queries, efficient joins, relational modeling, and query performance.
• Production software engineering experience with Python or TypeScript.
• Demonstrated ability to implement reliable data ingestion and integration workflows across APIs, databases, files, and third-party data sources.
• Experience with structured, semi-structured, and unstructured data, and sound judgment about storage and processing tradeoffs.
• Practical experience maintaining data integrity, building quality checks, and troubleshooting production pipelines.
• Ability to reconcile entities and identifiers across inconsistent datasets, including deduplication and mapping between sources.
• Familiarity with relational databases, cloud object storage, and modern data processing patterns.
• Comfort making independent technical decisions in an early-stage environment where systems and requirements evolve quickly.

Nice to have

• Experience working with AWS, PostgreSQL, or similar cloud and database services.
• Experience with a warehouse or orchestration stack such as Snowflake, BigQuery, dbt, or Airflow.
• Exposure to LLM-driven extraction, classification, labeling, and evaluation pipelines.
• Background in data-intensive SaaS, fintech, financial services, or other environments with demanding data-quality requirements.
• Experience helping establish a data engineering function at a startup.

• Take broad ownership of core data infrastructure at an early-stage AI company.
• Build new systems and shape architectural decisions rather than inherit a mature stack.
• Collaborate with product and AI engineers on production applications of machine learning and data enrichment.
• Work on problems where reliability, data quality, and clear provenance are business-critical.
• $170K–$200K base salary, plus eligibility for a performance-based cash bonus and an early-stage equity grant.
• Health coverage, flexible paid time off, and paid holidays.
• Full-time opportunity in New York City with regular in-person collaboration.