Senior Data Engineer

Summary

Join SEON, a leading fraud prevention company, as a Data Engineer and contribute to building and maintaining scalable data pipelines and systems. You will be responsible for processing structured and unstructured data, automating workflows, and managing cloud-based data platforms. This role requires strong technical skills, experience with big data tools, and a passion for data engineering. SEON offers a dynamic work environment with opportunities to work with a world-class research and development team and contribute to the company's mission of making the internet a safer space for everyone.

Requirements

• Experience in one or more of the following programming languages, python, Java, scala
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field
• 5+ years of experience in data engineering, software development, or related roles
• Demonstrated success in building and maintaining scalable data pipelines and systems
• Proficient in SQL and at least one programming language (e.g., Python, Scala, Java)
• Hands-on experience with big data tools like Spark, Hadoop, or Kafka
• Expertise with cloud platforms (AWS, Azure, GCP) and services such as S3, Lambda, and Dataflow
• Strong understanding of data modeling, warehousing, and architecture principles
• Ability to work with large datasets and solve complex data challenges
• Strong problem-solving and critical-thinking abilities
• Excellent collaboration and communication skills for working across teams
• Fluent English knowledge

Responsibilities

• Build, maintain, and optimize scalable ETL/ELT pipelines to process structured and unstructured data
• Automate workflows to ingest, transform, and store data in real-time or batch processing frameworks
• Architect and maintain cloud-based data platforms, such as Snowflake, BigQuery, or Redshift
• Manage and optimize relational and NoSQL databases for analytics and operational needs
• Partner with data scientists, analysts, and software engineers to deliver end-to-end data solutions
• Translate business requirements into data models, workflows, and engineering tasks
• Implement systems for data validation, lineage tracking, and quality monitoring
• Ensure data security, compliance, and privacy standards are met (e.g., GDPR, HIPAA, SOC 2)
• Monitor and enhance the performance of data systems to handle increasing scale and complexity
• Leverage big data tools (e.g., Apache Spark, Kafka) for processing and analytics
• Evaluate and implement emerging technologies to improve efficiency and performance
• Contribute to the establishment of best practices and reusable frameworks

Preferred Qualifications

• Having Streaming knowledge is a plus (e.g. Apache Flink, spark streaming)
• Familiarity with BI tools like Tableau, Looker, or Power BI is a plus
• Experience with real-time data processing systems and event-driven architectures
• Familiarity with machine learning pipelines and model deployment
• Knowledge of DevOps and CI/CD practices for data engineering