Job Title

Data Acquisition & Infrastructure Engineer

Job Description

Role Overview

As Data Acquisition & Infrastructure Engineer, you will be the foundation of MAI's data capabilities. Your primary focus will be the development and operation of automated data collection pipelines that aggregate publicly available information from across the art market ecosystem. You will also own the design and maintenance of the underlying database infrastructure — built on PostgreSQL and AWS — that stores and serves this data.

This is a high-ownership role. The infrastructure is partially built; you will take it to production scale. You will work closely with AI engineers, a computer vision engineer, a product manager, full-stack developers, and the Head of Art Research, reporting directly to the Head of AI Engineering.

Key Responsibilities

DATA PIPELINE & COLLECTION

Architect and maintain automated pipelines that collect, normalize, and ingest publicly available art market data from web-based sources
Build reliable, maintainable collection systems using Python (Scrapy, BeautifulSoup, Playwright, or equivalent), with a strong emphasis on resilience, scheduling, and data freshness
Manage pipeline orchestration and scheduling using tools such as Apache Airflow, AWS EventBridge, or cron
Navigate the practical challenges of large-scale public data collection, including access patterns, rate constraints, and source reliability
Handle messy, inconsistent real-world datasets — clean, transform, and standardize data for downstream consumption

DATABASE ENGINEERING

Design, build, and maintain relational database schemas in PostgreSQL (hosted on Amazon RDS) to support complex, multi-entity art market data — artists, works, transactions, provenance, and valuation history
Develop and optimize queries, indexes, and data models to ensure performance at scale
Establish and enforce data quality standards, validation rules, and integrity constraints across the database
Collaborate with AI engineers and the computer vision team to ensure the data layer supports model training and inference requirements

INFRASTRUCTURE & OPERATIONS

Deploy and manage pipeline workloads on AWS (Lambda, EC2, S3, RDS)
Monitor pipeline health, data freshness, and system reliability — proactively address failures
Contribute to infrastructure-as-code practices as the team scales

Restrictions

No telecommuting
No Agencies Please

Requirements

Core Requirements

3–5 years of professional experience in data engineering or a closely related discipline
Proven experience building and maintaining automated data collection pipelines from web-based public sources using Python (Scrapy, BeautifulSoup, Playwright, or Selenium)
Strong data cleaning and normalization skills, with demonstrated ability to handle heterogeneous, inconsistent real-world datasets
Solid PostgreSQL experience: schema design, query optimization, and database maintenance
Hands-on AWS experience: Lambda, EC2, S3, RDS
Experience scheduling and orchestrating data pipelines (Apache Airflow, AWS EventBridge, or equivalent)
Experience navigating the constraints of large-scale public data collection, including reliability, access patterns, and data freshness challenges

Nice to Have

Knowledge of data quality frameworks and validation pipeline design
Experience with containerization (Docker) and infrastructure-as-code (Terraform, AWS CDK)
Familiarity with ETL/ELT tooling (dbt, AWS Glue, or equivalent)
Exposure to art market platforms (Christie's, Sotheby's, Artsy, Artnet) or understanding of how auction and gallery data is structured
Background or genuine interest in the art world, collectibles, or alternative asset markets
Experience in a startup or early-stage environment where ownership and adaptability are essential

About the Company

About Master Art Index

Master Art Index (MAI) is building the definitive intelligence layer for the global art market. We develop AI-driven valuation models, structured databases, and financial-grade data infrastructure for blue-chip modern and contemporary artworks. Our platform sits at the intersection of finance, technology, and art — enabling institutional-quality analysis in one of the world's most opaque asset classes.

We are a cross-functional team of AI engineers, computer vision specialists, product managers, and full-stack developers. We move fast, value ownership, and build things that don't yet exist.

What We Offer

A technically differentiated problem set — art market data is sparse, inconsistent, and largely unstructured; making it machine-readable

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