III Staff Data Engineer

About the Company: WAG is transforming how art and collectibles are valued, managed, and traded. Born from the merger of Winston Art Group (the largest independent appraisal and advisory firm in the U.S.) and Artory (a pioneer in art tokenization), we combine deep industry expertise with technologies like AI and blockchain to modernize a $2.9 trillion global asset class. We're already generating significant revenue and recently raised our Series A from top-tier VCs. Now, we're building the next-generation platform to unlock liquidity, trust, and intelligence in the art market—and we're looking for exceptional engineers to help us do it. Why Join Us: Meaningful equity and competitive compensation. High-impact role at a growing company with revenue, funding, and a compelling vision Build at the intersection of art, fintech AI,and blockchain. A collaborative, pragmatic team that values speed, clarity, and technical quality. Remote-flexible culture with an HQ in NYC. Backend by top VCs and trusted by leading collectors, advisors, and institutions. Job Objective: We're hiring a Staff Data Engineer to architect and scale the systems that power our AI-driven valuation tools, market analytics, and collector insights. You'll lead development of our data ingestion pipelines, enrichment workflows, and metadata infrastructure—from scraping and parsing messy real-world sources to preparing structured datasets that feed public indices,machine learning models and collector-facing tools. This is a high-ownership and leadership role for someone who thrives at the intersection of data engineering, AI integration, and real-world asset intelligence. You'll work closely with backend engineers, domain experts, and product leadership to turn fragmented data into a competitive advantage. Responsibilities: Design and operate scalable data ingestion and web scraping systems Build batch and real-time pipelines to normalize, enrich, and version data across structured and unstructured sources Develop systems to support LLM- and ML-based document parsing, OCR, and classification Own the architecture of our data storage and processing stack, including PostgreSQL, data lakes, and data warehouses Operationalize AI/ML workflows by preparing clean training and inference datasets with robust lineage, validation, and error handling Integrate output with backend APIs, valuation services, and frontend analytics dashboards Collaborate across engineering and product to ship reliable, intelligent features quickly Contribute to our infrastructure tooling, including CI/CD, IaC (Terraform), and data observability Requirements: B.S in Computer Science or equivalent Experience 7+ years of backend engineering experience with at least 2+ years in a technical lead, staff, or principal role at a high-growth startup or product company. Leadership: Proven track record of mentoring engineers, leading technical initiatives, and making architectural decisions that scale. Fluent in Python, SQL, and familiar with orchestration tools like Airflow, Dagster, or Temporal Have designed or maintained web scraping pipelines at scale , using best practices around retries, proxies, and anti-bot strategies Understand tradeoffs between ETL and ELT, and have hands-on experience with data lakes and data warehouses Have integrated structured and unstructured sources, and enjoy resolving messy edge cases that come with real-world data Have worked with LLM APIs or ML models in production , particularly for document understanding, NLP, or entity extraction Thrive in AI-native environments and enjoy building tools that support intelligent automation and analytics Experience building products from scratch. Care about clean architecture, versioning, reproducibility, and quality in data systems Experience with GCP, Node.js/JavaScript and Vercel Preferred: Experience with art, collectibles, or other fragmented asset classes where clean data is rare but valuable Familiarity with vector databases and semantic search Experience with modern data stack tools (dbt, Fivetran, etc.) Knowledge of data governance and compliance requirements