Data Acquisition & Infrastructure Engineer (Montreal, QC, Canada)

Role Overview

As a Data Acquisition & Infrastructure Engineer, you will establish the bedrock of MAI's data capabilities. Your core responsibilities will involve developing and operating automated data collection pipelines to aggregate publicly available information from the art market ecosystem. You will also be responsible for the design and maintenance of the underlying database infrastructure, built on PostgreSQL and AWS, which stores and serves this data. This is a high-ownership position where you will scale existing, partially built infrastructure to production levels. You will collaborate 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 for collecting, normalizing, and ingesting publicly available art market data from web sources.
• Build reliable and maintainable collection systems using Python (Scrapy, BeautifulSoup, Playwright, or equivalent), prioritizing resilience, scheduling, and data freshness.
• Manage pipeline orchestration and scheduling using tools such as Apache Airflow, AWS EventBridge, or cron.
• Address the practical challenges of large-scale public data collection, including access patterns, rate constraints, and source reliability.
• Handle and transform messy, inconsistent real-world datasets, cleaning and standardizing data for downstream use.

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, valuation history).
• Develop and optimize queries, indexes, and data models for 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 adequately 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 addressing any failures.
• Contribute to infrastructure-as-code practices as the team scales.

Core Requirements

• 3–5 years of professional experience in data engineering or a closely related field.
• 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, including schema design, query optimization, and database maintenance.
• Hands-on AWS experience with services such as Lambda, EC2, S3, and RDS.
• Experience scheduling and orchestrating data pipelines using tools like Apache Airflow, AWS EventBridge, or equivalents.
• 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 (e.g., Christie's, Sotheby's, Artsy, Artnet) or an understanding of how auction and gallery data is structured.
• A 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 crucial.