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Backend Engineer NY

Job in New York, New York County, New York, 10261, USA
Listing for: Dl
Full Time position
Listed on 2026-03-01
Job specializations:
  • IT/Tech
    Data Engineer, Cloud Computing
Salary/Wage Range or Industry Benchmark: 80000 - 100000 USD Yearly USD 80000.00 100000.00 YEAR
Job Description & How to Apply Below
Position: Backend Engineer (New York, NY | Full-time)
Location: New York

We are looking for a full-time Backend Engineer to build out Godel Terminal, a professional web-based financial terminal software offering comprehensive financial and investment data.

Stack:

Monitoring/Visualization: Grafana, Prometheus

Responsibilities:

Design, build, and own scalable streaming data pipelines and ETL workflows

Optimize large-scale, high-performance databases and ensure efficient data storage

Integrate high-volume data feeds and process them for real-time applications

Deploy and manage containerized data infrastructure using Docker and Kubernetes

Collaborate with backend teams to ensure real-time data delivery to UI components

Monitor system performance and ensure reliability through tools like Grafana and Prometheus

Implement data security best practices and monitor service reliability with alerting and logging

Requirements:

NYC residence or willingness to relocate

Strong proficiency in Python and JVM-based languages (Kotlin or Java)

Extensive experience with large-scale data processing frameworks such as Apache Beam or Spark

Expertise in database management (Postgre

SQL, No

SQL solutions) and query optimization

Familiarity with event-driven architectures and messaging systems like Kafka

Hands-on experience with cloud services (AWS, GCP) and Infrastructure as Code tools (Terraform, Cloud Formation)

Experience with monitoring tools like Grafana and Prometheus

Strong debugging skills, especially focused on distributed systems performance optimization

Excellent communication skills, both written and verbal

Nice to have:

Experience with real-time data systems and streaming architectures (e.g., Kafka, Flink)

Familiarity with financial data systems or terminals

Production experience with Kubernetes at scale in cloud environments

Background in managing machine learning pipelines or complex data workflows

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