Big Data Solutions Architect

Lead Big Data Engineer

We are seeking a talented individual to take on the role of Lead Big Data Engineer, where you will be responsible for delivering key features of our Enterprise TruRisk Platform. This platform enables customers to effectively Measure, Communicate and Eliminate Cyber Risks.

This is a fantastic opportunity to be an integral part of a team building our next-generation platform using Big Data & Micro-Services based technology to process over billions of transactions data per day, leverage open-source technologies, and work on challenging and business-impacting initiatives.

The ideal candidate will have experience in designing, developing and supporting highly scalable, distributed SaaS-based Security Risk Prioritization products.

In this role, you will:

• Provide technical leadership to the engineering organization on data platform design, roll out and evolution.
• Collaborate with product teams, professional services and sales engineers on solution and trade-off reviews and represent engineering in such conversations.
• Drive technology explorations and roadmaps.
• Serve as a technical lead on our most demanding, cross-functional departments.
• Ensure the quality of architecture and design of systems.

You will also be expected to:

• Functionally decompose complex problems into simple, straight-forward solutions.
• Fully and completely understand system interdependencies and limitations.
• Possess expert knowledge in performance, scalability, enterprise system architecture, and engineering best practices.
• Leverage knowledge of internal and industry prior art in design decisions.
• Effectively research and benchmark cloud technology against other competing systems in the industry.
• Able to document the details so it will be easy for developers to understand the requirements.
• Assisting developers with proper requirements and directions.

The successful candidate will possess:

• Bachelor's degree in computer science or equivalent.
• 8+ years of total experience.
• 4+ years of relevant experience in design and architecture of Big Data solutions using Spark.
• 3+ years of experience in working with engineering resources for innovation.
• 4+ years of experience in understanding Big Data events flow pipeline.
• 3+ years of experience in performance testing for large infrastructure.
• 3+ In depth experience in understanding various search solutions solr/elastic.
• 3+ years of experience in Kafka.
• In depth experience in Data lakes and related ecosystems.
• In depth experience of messing queue.
• In depth experience in giving requirements to build a scalable architecture for Big Data and Micro-services environments.
• In depth experience in understanding caching components or services.
• Knowledge in Presto technology.
• Knowledge in Airflow.
• Hands-on experience in scripting and automation.
• In depth understanding of RDBMS/NoSQL, Oracle , Cassandra , Kafka , Redis, Hadoop, lambda architecture, kappa , kappa ++ architectures with flink data streaming and rule engines.
• Experience in working with ML models engineering and related deployment.
• Design and implement secure big data clusters to meet many compliances and regulatory requirements.
• Experience in leading the delivery of large-scale systems focused on managing the infrastructure layer of the technology stack.
• Strong experience in doing performance benchmarking testing for Big data technologies.
• Strong troubleshooting skills.
• Experience leading development life cycle process and best practices.
• Experience in Big Data services administration would be added value.
• Experience with Agile Management (SCRUM, RUP, XP), OO Modeling, working on internet, UNIX, Middleware, and database related projects.
• Experience mentoring/training the engineering community on complex technical issue.
• Project management experience.

Key Responsibilities:

• Data Platform Design and Development.
• Technical Leadership.
• Collaboration and Communication.
• Technology Exploration and Roadmap.
• System Quality and Architecture.
• Problem Decomposition and Solution.
• System Understanding and Limitations.
• Expert Knowledge and Best Practices.
• Research and Benchmarking.
• Documentation and Requirements.