Staff Data Engineer

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. About the role Location US Remote or Hybrid (East Coast or Central time-zone required). For candidates in NY or Miami, an interview may be conducted in person. The Role We’re hiring a Staff Data Engineer to be the founding technical leader of our data team. You’ll build the data platform from the ground up—the engine that powers WAG’s AI-driven valuations, market analytics, and collector intelligence. As the first dedicated data engineering hire, you’ll own the entire data architecture—from ingestion and scraping infrastructure to enrichment pipelines, data warehousing, and the datasets that feed our machine learning models and public indices. This is a team-founding, tech lead role : you’ll lay the technical foundation, establish data engineering standards and culture, hire your team, and scale the data platform alongside the company. You’ll report directly to our Head of Engineering , a highly hands‑on technical leader who you’ll partner closely with on data architecture decisions and technical strategy. Together, you’ll co‑own the data platform vision —balancing immediate pipeline needs with long‑term scalability, quality, and governance. Our Head of Engineering is deeply involved in code, design reviews, and technical discussions, so you’ll have a close working relationship focused on building world‑class data systems. You’ll also work closely with our CPO (Chief Product Officer), domain experts, and company leadership to turn fragmented, messy real‑world data into a durable competitive advantage. This isn’t just about building pipelines—it’s about making architectural decisions on storage, processing, and quality; evaluating tradeoffs between speed and rigor; and building a data platform that can evolve from early‑stage to enterprise‑scale. This is an AI‑native environment. We move fast using tools like Cursor and Claude Code, build with LLM APIs from OpenAI and others, and actively leverage AI in our product and development workflows. If you’re excited about being a founding technical leader who ships production data systems with AI as a core tool, you’ll thrive here. What You’ll Do Team Building & Technical Leadership: 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 Hands‑On Development: 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 Cross‑Functional Collaboration: 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 You Might Be a Fit If You Required Education: B.S. in Computer Science or equivalent Experience: 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 Leadership: Proven track record of building or scaling data teams , mentoring engineers , and making foundational architectural decisions that set the direction for an entire data organization Technical Skills 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 Mindset You balance pragmatism with data quality —you know when to move fast and when to invest in governance and reliability You have a bias toward clean architecture and can articulate tradeoffs between speed, cost, and correctness You’re excited about AI tooling and actively use tools like Cursor, Claude, or Copilot to increase velocity You’re thriving in ambiguity and can chart technical direction for the data platform with incomplete information You’re energized by building from zero —you want to lay the foundation, not inherit someone else’s Preferred: Experience building 0→1 data platforms at early‑stage startups (seed through Series B) Prior experience founding or building a data team from scratch—hiring, onboarding, and establishing team processes Prior tech lead or staff engineer experience at a Series A+ company Experience with data warehousing (Snowflake, BigQuery, Redshift) and modern data stack tools (dbt, Fivetran, etc.) Familiarity with vector databases and semantic search (Pinecone, Weaviate, pgvector) Experience with ML/AI model deployment and managing inference costs/latency in data pipelines Domain knowledge in art, collectibles, fintech, or fragmented asset classes where clean data is rare but valuable Experience with data governance and compliance requirements We offer great benefits, including: A Welcoming Team A friendly, international, agile team that works together with cutting‑edge technologies 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 Compensation Competitive salary based on experience and skills Discretionary performance‑based bonuses Equity option grants of company shares, offering alignment with company success Health, Wellness, and Benefits 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 #J-18808-Ljbffr