Senior Backend Engineer

 Types of Projects and Impact:

• Collaborate with the Smart Signals Product team to improve fraud detection signals, including browser bot detection, VM detection, VPN detection, and more.
• Conduct deep dives into problematic features, researching and analyzing their behavior to understand root causes and identify potential solutions. 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.

Position Overview:

• As a Senior Backend Engineer with Data Science skills, you will be responsible for developing and maintaining backend services for fraud detection. Your role will focus on end-to-end engineering, from analyzing traffic and building scalable data pipelines to writing production-ready code and deploying it in production environments.

Required Skills:

• BS/MS in Computer Science, Data Science, or a related field, or equivalent work experience.
• 3+ years of experience in backend development with exposure to data science.
• Backend Engineering Expertise:
• 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), with working knowledge of data engineering practices.
• 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.).
• For future projects, machine learning knowledge may be a plus:
• 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.