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Data Engineer
В офисеПолная занятостьData EngineeringFinancial Services
Join a fraud prevention data engineering team building reliable, scalable data assets and pipelines for analytics, research, and fraud reduction. The role involves close collaboration with analysts, data scientists, software developers, and product stakeholders.
Обязанности
- Gather data requirements from internal customers and translate them into databases and analytical software.
- Design and develop batch and real-time ETL pipelines using big data technologies.
- Own data assets end to end, including data quality, operational support, and working-hours on-call coverage.
- Influence architecture and development practices for data and analytics solutions.
- Participate in system design and code reviews.
- Serve as a subject-matter resource for data exploration, pipelines, and analytics.
- Collaborate with distributed teams and contribute to fraud prevention experiments and research.
Требования
- 1–2 years of relevant data engineering experience, or equivalent experience.
- Expertise in SQL within a large-scale data warehouse, data lake, or lakehouse environment.
- Experience developing end-to-end ETL pipelines, including monitoring and orchestration.
- Proficiency with Spark and experience with technologies such as Databricks, Presto, Athena, Trino, or Redshift.
- Experience with cloud environments, preferably AWS, including services such as EMR, S3, and EC2.
- Proficiency in Python and shell scripting in a Linux environment.
- Knowledge of database design, entity relationship or dimensional modeling, and data integration.
- Experience with logging and monitoring tools such as Splunk or Grafana.
- Strong business understanding and the ability to translate product and internal customer needs into data solutions.
- Bachelor’s degree in Computer Science or Industrial Engineering, or equivalent relevant experience.
- High-level English and Hebrew.
- Experience with agentic development environments and data-focused agentic workflows.
Будет плюсом
- Experience with fraud or cybersecurity systems.
- Knowledge of data science or machine learning.
- Experience with NoSQL databases such as DynamoDB or Cassandra.
- Knowledge of Scala or Java.
- Experience with stream processing or near-real-time ingestion.
- Knowledge of graph databases.