Staff Data Engineer

Role Overview

• Build the data team from scratch —define the hiring roadmap, recruit and onboard your first 2–3 data engineers, and establish the team’s culture, standards, and ways of working
• Own the entire data platform architecture from day one—make the critical decisions on storage layers, processing frameworks, orchestration, and data modeling patterns
• Define technical standards and best practices for data quality, testing, documentation, lineage, and governance
• Lead system design for complex problems involving large-scale ingestion, entity resolution, LLM-powered data extraction, and real-time analytics
• Evaluate and adopt new technologies that improve data velocity, quality, reliability, or capabilities
• Establish data governance frameworks including versioning, reproducibility, validation, and compliance
• Design and operate scalable data ingestion and web scraping systems, including best practices around retries, proxies, rate limiting, and anti-bot strategies
• Build batch and real-time pipelines to normalize, enrich, deduplicate, and version data across structured and unstructured sources
• Architect systems to support LLM
• and ML-based document parsing, OCR, entity extraction, and classification at scale
• Own the data storage and processing stack, including PostgreSQL, data lakes, data warehouses, and vector databases
• Operationalize AI/ML workflows by preparing clean training and inference datasets with robust lineage, validation, and error handling
• Design and maintain data models that serve backend APIs, valuation services, analytics dashboards, and public indices
• Contribute to infrastructure tooling, including CI/CD, IaC (Terraform), data observability, and cost management
• Co-own data platform vision with Head of Engineering: collaborate daily on architecture, technical roadmap, and engineering standards
• Partner with backend engineers to define API contracts, data serving patterns, and integration points between pipelines and application services
• Collaborate with product and domain experts to translate business requirements into reliable, well-modeled datasets
• Work with company leadership (Head of Engineering, CPO, President) on data strategy, hiring, and long-term platform vision
• Communicate technical decisions clearly to non-technical stakeholders

Requirements

• B.S. in Computer Science or equivalent
• 7+ years of data engineering experience with at least 2+ years in a technical lead, staff, or principal role at a high-growth startup or product company
• Expert in Python and SQL, with deep understanding of performance, data modeling, and processing patterns
• Strong database expertise (PostgreSQL or similar) including query optimization, schema design, indexing, and partitioning strategies
• Deep experience with pipeline orchestration tools like Airflow, Dagster, Prefect, or Temporal
• Hands-on experience designing and maintaining web scraping systems at scale, including retries, proxies, and anti-bot strategies
• Production experience integrating structured and unstructured sources, with a track record of resolving messy, real-world data challenges
• Hands-on experience with LLM/AI integration in data workflows —you’ve built pipelines using OpenAI, Anthropic, or open-source models for document understanding, NLP, entity extraction, or classification
• Deep knowledge of data architecture patterns including ETL vs. ELT, data lakes vs. warehouses, batch vs. streaming, and schema evolution
• Production experience with AWS (or GCP/Azure) including compute, storage, networking, and managed data services
• Strong DevOps fundamentals: Docker, Terraform, CI/CD, and data observability/monitoring

Tech Stack

• Airflow
• AWS
• Azure
• Docker
• ETL
• Google Cloud Platform
• Postgres
• Python
• SQL
• Terraform

Benefits

• A Welcoming Team
• Generous paid time off, including vacation, sick days, and holidays
• Paid parental leave (maternity, paternity, adoption, leave)
• Paid volunteer days to encourage community involvement
• Collaborative, innovative, and inclusive company culture
• Employee recognition and appreciation programs
• Team-building activities and social events
• Transparent communication and feedback channels
• Competitive salary based on experience and skills
• Discretionary performance-based bonuses
• Equity option grants of company shares, offering alignment with company success
• Comprehensive health insurance (medical, dental, and vision) with employees covered 100%
• On-site in our NY HQ fitness center and sauna
• Generous leave policies, including bereavement and reproductive loss leave
• Opportunities for continuous learning and training
• Mentorship programs and leadership development initiatives
• 401(k)
• Life insurance and disability coverage