Data Engineer
New York, NY

Job Description

About Birchbox:

We started Birchbox in 2010 to redefine the way consumers discover and shop for beauty and grooming. The company quickly grew from an exciting idea to a business that has materially shaped the beauty industry: we've activated an enormous group of underserved, untapped consumers, awakening their relationship with beauty by making the experience relevant, easy and fun. Our innovation isn't the simple concept of delivering a box of samples - it's understanding that although not everyone is passionate about beauty and grooming, everyone still deserves to have a great experience finding, trying and buying it.

Birchbox operates in six countries, reaching more than 2.5 million active customers with a portfolio of 500 best-in-class prestige brand partners. We have retail locations within select Walgreens stores across the US as well as a flagship Birchbox store in Paris.

About the role:

Birchbox is seeking an ambitious and experienced Data Engineer to help evolve our data systems as we continue to grow. In addition to fueling our recommender system and beauty box optimization toolkit, our data services enable and support decision-making company-wide. This software engineering role will be very impactful as we build out the next generation of our data services, and will have considerable leeway in leading architectural direction.

You'll be joining a lean Agile team supporting Data Infrastructure and Machine Learning. Primary upcoming initiatives include refreshing our algorithmically-driven subscription box optimization system, iterating on our e-commerce recommender services, retooling our event data collection systems, and supporting the next generation of our A/B testing machinery. Each of these projects will involve some DevOps work and some amount of data extraction and loading, but will also involve substantial backend software development.

Responsibilities

As a Birchbox Engineer, you will:

* Build fault-tolerant, scalable batch and real-time distributed data processing systems to drive our personalization and BI systems.
* Design and configure hosted and cloud-based data and machine learning infrastructure (Hadoop, Spark, AWS EMR).
* Implement fault tolerant data integrations between internal systems and with third-party APIs as needed, supporting product and marketing needs.
* Build and support internal A/B testing tooling for the engineering and product teams.
* Support the Business Intelligence team as needed, advising them on data provenance and reliability.
* Design and build components of internal tooling used by our subscription operations team (Python, MILP, CP).
* Tool our systems for observability, including logging, metrics monitoring, and dashboarding (Datadog).
* Contribute to maintaining an open, empowering, responsible, and proactive engineering culture.

About You

Ideally, we're seeking somebody with:

* 3+ years of professional software experience (or equivalent).
* Bachelor's degree in Computer Science or related field (or equivalent experience).
* Expert in Python, and comfortable with at least two other languages (e.g., Java, Ruby, PHP).
* Strong command line skills for working within virtualized machines (bash, tmux / screen, vim / emacs).
* Experience orchestrating data infrastructure (e.g., Pig/Hive, HBase, Spark, S3, Kafka/Kinesis, Redshift).
* Experience with modern workflow management systems (e.g., Airflow, Luigi, Azkaban).
* Advanced SQL skills (MySQL and/or Postgres), familiarity with data warehousing and dimensional modeling.
* Familiarity with non-relational data stores and/or indexes (e.g., MongoDB, HDFS, Cassandra, Solr, ElasticSearch).
* Experience working on teams using distributed version control (e.g., Git, Mercurial).
* Insatiable curiosity, a commitment to precision and excellence, and a desire to work collaboratively within and between teams.

Bonus Points

We'll definitely want to have a conversation if:

* You're committed to enabling DevOps culture with development, deployment, and observability / monitoring tooling.
* You've read more IETF RFCs than you'd admit in polite company.
* You're opinionated about when and how to leverage different algorithms, data manipulation techniques, and frameworks.
* You're passionate about idiomatic code style, readability, and when to use which software design patterns.