Data Infra Engineer

BioCatch is the leader in Behavioral Biometrics, a technology that leverages machine learning to analyze an online user’s physical and cognitive digital behavior to protect individuals online. BioCatch’s mission is to unlock the power of behavior and deliver actionable insights to create a digital world where identity, trust, and ease coexist.Today, 32 of the world's largest 100 banks and 210 total financial institutions rely on BioCatch Connect™ to combat fraud, facilitate digital transformation, and grow customer relationships.. BioCatch’s Client Innovation Board, an industry-led initiative including American Express, Barclays, Citi Ventures, and National Australia Bank, helps BioCatch to identify creative and cutting-edge ways to leverage the unique attributes of behavior for fraud prevention. With over a decade of analyzing data, more than 80 registered patents, and unparalleled experience, BioCatch continues to innovate to solve tomorrow’s problems. For more information, please visit www.biocatch.com.

Main responsibilities:

• Data Architecture Direction: Provide strategic direction for our data architecture, selecting the appropriate componments for various tasks. Collaborate on requirements and make final decisions on system design and implementation.
• Project Management: Manage end-to-end execution of high-performance, large-scale data-driven projects, including design, implementation, and ongoing maintenance.
• Cost Optimization: Monitor and optimize cloud costs associated with data infrastructure and processes.
• Efficiency and Reliability: Design and build monitoring tools to ensure the efficiency, reliability, and performance of data processes and systems.
• DevOps Integration: Implement and manage DevOps practices to streamline development and operations, focusing on infrastructure automation, continuous integration/continuous deployment (CI/CD) pipelines, containerization, orchestration, and infrastructure as code. Ensure scalable, reliable, and efficient deployment processes.
  
  

• 5+ Years of Experience: Demonstrated experience as a DevOps professional, with a strong focus on big data environments, or Data Engineer with strong DevOps skills.
• Data Componments Management: Experience with managing and design data infrasturacture such as Snowflake, PostgreSQL, Kafka, Aerospike, Object Store.
• DevOps Expertise: Proven experience in creating, establishing, and managing big data tools, including automation tasks. Extensive knowledge of DevOps concepts and tools, including Docker, Kubernetes, Terraform, ArgoCD, Linux OS, Networking, Load Balancing, Nginx etc.
• Programming Skills: Proficiency in programming languages such as Python and Object-Oriented Programming (OOP) languages with an emphasis on the big data processing (like PySpark). Experience with scripting languages like Bash and Shell for automation tasks.
• Cloud Platforms: Hands-on experience with major cloud providers such as Azure, Google Cloud, or AWS.
  
  

• Performance Optimization: Experience in optimizing performance for big data tools and pipelines - Big Advantage.
• Security Expertise: Experience in identifying and addressing security vulnerabilities within the data platform - Big Advantage.
• CI/CD Pipelines: Experience in designing, implementing, and maintaining Continuous Integration/Continuous Deployment (CI/CD) pipelines – Advantage.
• Data Pipelines: Experience in building big data pipelines - Advantage.