דרושים Data Engineer, Fraud Prevention בתל אביב

About the Company:

Sunbit builds financial technology for real life. Our technology eases the stress of paying for life’s expenses by giving people more options on how and when they pay. Founded in 2016, Sunbit offers a next-generation, no-fee credit card that can be managed through a powerful mobile app, as well as a point-of-sale payment option available at more than 16,000 service locations, including auto dealership service centers, optical practices, dentist offices, veterinary clinics, and specialty healthcare services. Sunbit was included on the 2022 Inc. 5000 list. The financial technology company has also been named as a Most Loved Workplace®, Best Point of Sale Company, and as a Top Fintech Startup by CB Insights. We use cutting-edge innovations in financial technology to bring leading data and features that allow individuals to be qualified instantly, making purchases at the point-of-sale fast, fair and easy for consumers from all walks of life. We create value focused on our core values; we work tirelessly to ensure that Sunbit becomes available to everyone, everywhere.

About the Fraud Prevention Squad:

The Fraud Prevention Squad is a highly skilled and collaborative team of frontend and backend engineers, analysts and data scientists working to develop cutting-edge fraud detection and prevention systems. The team is passionate about building secure financial products and fosters a culture of learning and mentorship, where everyone is encouraged to share their expertise and grow their skills.

About the role

As a Data Engineer on Sunbit's Fraud Prevention Squad, you will play a key role in ensuring the integrity of our data offerings. Your main responsibility will be to design, build, and enhance data pipelines to allow detection of fraudulent activities across all Sunbit products. You’ll work closely with fraud data scientists and backend engineers to operationalize features and maintain real-time fraud signals. Additionally, you will continuously improve existing technologies, integrate fraud prevention measures seamlessly into our products.

Responsibilities:

• You’ll be responsible for designing, building, and maintaining all data solutions powering fraud detection. The ideal candidate is a hands-on professional with strong knowledge of data pipelines and an ability to translate business needs into flawless data flow.
• This is a great opportunity to own the entire fraud data pipeline and architecture from day one.
• Create ELT/Streaming processes and SQL queries to bring data to/from the data warehouse and other data sources.
• Own the data lake pipelines, maintenance, improvements and schema.
• Comfortable joining backend development efforts.
• Develop and improve data-driven fraud features used in production fraud models and rule engines.
• Collaborate with various stakeholders across the company like data developers, analysts, data science, etc, in order to deliver team tasks. Work closely with all business units and engineering teams to develop a strategy for long-term data platform architecture.
• Ensure adherence to coding best practices and development of reusable code
• Constantly monitor data platform and make recommendations to enhance system architecture

Requirements:

• 4+ years of experience as a Data Engineer
• 4+ years of direct experience with SQL/NoSQL (e.g. MySQL/MongoDB/Postgres), data modeling, data warehousing, and building ELT/ETL pipelines - MUST
• 2+ years of Python/ NodeJS experience.
• 3+ years of experience in scalable data architecture, fault-tolerant ETL, and monitoring of data quality in the cloud
• Experience working with cloud environments (AWS preferred) and big data technologies (EMR,EC2, S3, Snowflake , spark-streaming, hive, DBT)
• Exceptional troubleshooting and problem-solving abilities, debugging, and root causing defects in large scale systems.
• Deep understanding of distributed data processing architecture and tools such as Kafka and Spark and Airflow
• Experience with design patterns and coding best practices, understanding of data modeling concepts, techniques and best practices
• Proficiency with modern source control systems, especially Git
• Basic Linux/Unix system administration skills
• B.Sc in computer science or equivalent experience.

Nice to have

• Experience in Software Engineering, Kotlin/Python
• Experience in Spark - In-depth knowledge in Apache Spark and the broader Data Engineering ecosystem
• Experience with data warehouses
• Understanding fintech business processes
• DevOps - AWS.
• Microservices
• Experience in DBT