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Discover and buy art from leading galleries and auctions around the world

Data Engineer

$100k – $145k AngelList Est.
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We are looking for an experienced Data Engineer to join the Platform team at Artsy, who will help develop core pipelines and algorithms that will allow us to incorporate, process, and act on data flowing through our platform. This can involve challenges like data warehousing, natural language processing, and machine learning.

The Platform team works on shared infrastructure enabling our many-faceted product, and on shared tooling for the wider company. This includes the core API as well as supporting services such as search, recommendations, and market insights.

We are a lean team that relies on Ruby for back-end services, Python for machine learning, Scala for data processing, and SQL for enrichment and analysis. Our stack includes Postgres, Elasticsearch, Redshift, RabbitMQ, Spark, and Hive. We integrate with services like Segment and Looker to accomplish our work, and are always evaluating new tools, technologies, and trends.

As a data engineer you'll design pipelines, databases, and APIs that will evolve with our product and business needs. You'll architect systems for scale, and you'll think about how to keep our complex, distributed platform resilient to data integrity issues or service disruptions. You'll help structure our data so it's an invaluable asset in our dynamic business environment.

Some areas you’ll be expected to contribute to in the coming months:
- Design and optimize Elasticsearch queries to enable art collectors to find the work they love instantly.
- Develop machine learning models that aid us in ranking, classification, and predictive tasks.
- Transform the growing body of art market data we've collected into meaningful insights about artists, artworks, and trends.
- Develop and scale our recommendations and similarity algorithms.
- Empower a data-driven culture by making high-volume activity data rapidly available for analysis.

Approximately 5 years professional experience with some or all of the following is desirable:

- Scaling data pipelines and databases.
- Designing, deploying and maintaining distributed data stores.
- Large-scale data processing or enrichment.
- Hadoop MapReduce technology.
- Tuning production scale search engines.

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