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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

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.


  • 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


  • 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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