2 месяца назад
Team Lead Data Pipeline Engineer (Cybersecurity)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Team Lead Data Pipeline Engineer (Cybersecurity): Leading the Data Pipeline engineering team to build and optimize high-scale streaming and batch processing systems with an accent on API security and threat detection. Focus on designing complex distributed systems, managing high data volumes, and improving visibility into security postures.
Company
is a pioneering API Security vendor utilizing big data and AI to identify and prevent API attacks.
What you will do
- Lead the Data Pipeline engineering team, fostering a culture of innovation and high motivation.
- Design and develop high-scale distributed systems for streaming and batch processing.
- Build and optimize data-oriented applications capable of handling massive rates and volumes.
- Develop methods to accurately analyze traffic patterns and identify attack vectors.
- Collaborate with the R&D team to provide customers with improved visibility into their security posture.
Requirements
- 5+ years of software engineering experience, with 3+ years in Scala, Java, or Go.
- 2+ years of experience leading an engineering team of 3 or more engineers.
- Proven experience in data pipeline development for streaming and batch processing.
- Strong expertise in designing and debugging complex distributed systems (microservices, event-driven).
- Experience developing large-scale data applications with high throughput and volume.
- Bachelor's degree in Computer Science or equivalent professional background.
Nice to have
- Hands-on experience with Kafka and its ecosystem (Streams, Connect).
- Experience with No-SQL columnar databases.
- Experience building microservices within AWS.
Culture & Benefits
- Startup environment with the ability to see immediate results of work.
- Opportunity to make an outsized impact on company growth.
- Collaborative team culture that embraces new ideas from all levels.
- Opportunity to solve diverse and challenging big data problems at scale.
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