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Data Engineer - Top eCommerce Startup at Spotfront (New York, NY)

Spotfront delivers intelligent vendor marketing solutions built for the next generation of e-commerce. Our solutions help retailers implement, automate, and scale brand-funded marketing programs on e-commerce sites. Spotfront is a New York City-based technology company that works with the US's largest e-commerce retailers. Learn more about us and PromoteIQ, our flagship product, at http://www.spotfront.com.


Who we’re looking for


At Spotfront, Data plays an integral role and software engineers on our data engineering team build the pipelines that supply critical data to our e-commerce promotions platform.  The infrastructure and applications that you'll build on the data engineering team will have broad and critical reach in powering real-time auction decisions, becoming multipliers on our revenues, and forecasting supply and demand for our customers.


Responsibilities



  • Ship high-quality, well-tested, secure, and maintainable code

  • Design, develop, and maintain data pipelines and back-end services for real-time decisioning, reporting, optimization, data collection, and related functions

  • Manage automated unit and integration test suites

  • Work collaboratively and communicate effectively with a small, motivated team of engineers and product managers

  • Experiment with and recommend new technologies that simplify or improve Spotfront's stack

  • Participate in an on-call rotation and work occasional off-hours


Qualifications



  • BS/MS in Computer Science or a related technical field or relevant equivalent work experience

  • Seeking candidates with 5+ years of experience in:

  • Architecting, building, and maintaining end-to-end, high-throughput data systems and their supporting services

  • Designing data systems that are secure, testable, and modular, particularly in Python, as well as their support infrastructure (shell scripts, job schedulers, message queues, etc.)

  • Designing efficient data structures and database schemas

  • Working with distributed systems architecture

  • Using profiling tools, debugging logs, performance metrics, and other data sources to make code- and application-level improvements

  • Developing for continuous integration and automated deployments

  • Utilizing a variety of data stores, including data warehouses (ideally Redshift), RDBMSes (ideally MySQL), in-memory caches (ideally Aerospike and Redis), and searchable document DBs (ideally Elasticseach)

  • Wrangling large-scale data sets 


by via developer jobs - Stack Overflow
 

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