Data Acquisition & Infrastructure Engineer
Job in
Montreal, Montréal, Province de Québec, Canada
Listing for:
Iconic Art AI
Full Time
position
Listed on 2026-07-27
Job specializations:
-
Software Development
Data Engineering, AWS
Job Description & How to Apply Below
Location: MontrealJob 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 sourcesBuild reliable, maintainable collection systems using Python (Scrapy, Beautiful Soup, Playwright, or equivalent), with a strong emphasis on resilience, scheduling, and data freshnessManage pipeline orchestration and scheduling using tools such as Apache Airflow, AWS Event Bridge, or cronNavigate the practical challenges of large-scale public data collection, including access patterns, rate constraints, and source reliabilityHandle messy, inconsistent real-world datasets — clean, transform, and standardize data for downstream consumptionDATABASE 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 historyDevelop and optimize queries, indexes, and data models to ensure performance at scaleEstablish and enforce data quality standards, validation rules, and integrity constraints across the databaseCollaborate with AI engineers and the computer vision team to ensure the data layer supports model training and inference requirementsINFRASTRUCTURE & OPERATIONS
Deploy and manage pipeline workloads on AWS (Lambda, EC2, S3, RDS)Monitor pipeline health, data freshness, and system reliability — proactively address failuresContribute to infrastructure-as-code practices as the team scalesRestrictions
No telecommutingNo Agencies PleaseRequirements
Core Requirements
3–5 years of professional experience in data engineering or a closely related disciplineProven experience building and maintaining automated data collection pipelines from web-based public sources using Python (Scrapy, Beautiful Soup, Playwright, or Selenium)Strong data cleaning and normalization skills, with demonstrated ability to handle heterogeneous, inconsistent real-world datasetsSolid PostgreSQL experience: schema design, query optimization, and database maintenanceHands-on AWS experience:
Lambda, EC2, S3, RDSExperience scheduling and orchestrating data pipelines (Apache Airflow, AWS Event Bridge, or equivalent)Experience navigating the constraints of large-scale public data collection, including reliability, access patterns, and data freshness challengesNice to Have
Knowledge of data quality frameworks and validation pipeline designExperience 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 structuredBackground or genuine interest in the art world, collectibles, or alternative asset marketsExperience in a startup or early-stage environment where ownership and adaptability are essentialAbout 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…
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