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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