Staff Backend Engineer (Data Science skills)

Overview

Job Description

Responsibilities

• - Design and implement scalable, high-performance backend systems
• - Ensure smooth integration with other components
• - Take ownership of features from concept to final deployment
• - Work closely with cross-functional teams to deliver reliable and robust solutions
• - Analyze large datasets of traffic to uncover patterns and detect fraudulent activity
• - Leverage data engineering techniques to process and manage large traffic datasets efficiently
• - Collaborate with the Smart Signals Product team to improve fraud detection signals
• - Conduct deep dives into problematic features
• - Develop hypotheses, run experiments, analyze results, and translate findings into actionable engineering improvements
• - Build and enhance backend systems for real-time data processing
• - Foster a data-driven culture by sharing engineering best practices and collaborating on cross-functional projects

Required Skills

• - BS/MS in Computer Science, Data Science, or a related field, or equivalent work experience
• - 8+ years of experience in backend development with exposure to data science
• - Strong experience in designing, developing, and maintaining scalable backend systems
• - Experience working with real-time data processing and APIs
• - Excellent coding skills, particularly in GoLang (or equivalent)
• - Strong knowledge of SQL and experience with databases like DynamoDB, Redis, or Elasticsearch
• - Proficiency with general software engineering tools: Git, IDEs, shell scripting, CI/CD
• - Proficient in English for clear communication in a global, remote team
• - Nice to have: Practical experience with analytical storage systems like ClickHouse, Snowflake, BigQuery, Redshift, or Databricks
• - Experience with data transformation frameworks like dbt or other data pipeline tools
• - Familiarity with data visualization tools such as Apache Superset, Tableau, or Looker
• - Experience with the Python data analytics stack (NumPy, Pandas, Jupyter, etc.)
• - Familiarity with supervised and unsupervised learning methods
• - Experience working with machine learning pipelines, model deployment, and performance monitoring
• - Understanding of core ML concepts such as feature engineering, model evaluation, and real-time inference

Benefits

• - Inclusive work environment
• - Remote work
• - Encouragement for people from underrepresented groups in tech to apply