The Big Data & Machine Learning team is highly visible within the company, at the front line of many of our customers' experience. The e-commerce space is dynamic, both in terms of customer experience and technologies, and we are looking for a strong technical and people manager to lead this team. You will coach and mentor your data engineering team, work closely with Product Management, Machine Learning Engineering and Data Science colleagues to define, refine, and drive delivery, facilitate and own technical decisions, and design, develop and deploy code into production. Radial's engineering managers are very technical and hands-on..
- Rapidly architect, design, prototype, implement and optimize architectures to tackle the Big Data and Data Science needs for the organization
- Guide an engineer team to develop modular code base to solve real world problems
- Lead regular peer code reviews to ensure code quality and compliance following best practices in the industry
- Work in cross-disciplinary teams within Radial to understand client needs and ingest rich data sources (transactional data, historical data, financial data, and operational data)
- Research, experiment, and utilize leading Big Data methodologies (Hadoop, HDP, Cloudera, Spark, Kafka, Netezza -IIAS and AWS) with cloud/on premise/hybrid hosting solutions
- Architect, implement, and test data processing pipelines, and data mining/data science algorithms on a variety of hosted settings (AWS, Azure and Radial’s own clusters)
- Provide proficient documentation and operating guidance for users of all levels
- Translate advanced business analytics problems into technical approaches that yield actionable recommendations across multiple and diverse domains
- Communicate results and educate others through design and build of insightful visualizations, reports and presentations
- Develop skills in capturing business requirement and translation, hypothesis-driven consulting, work stream and project management and client relationship development
- Help drive the process for pursuing innovations, target solutions, and extendable platforms for Radial Participate in developing and presenting thought leadership, and assist in ensuring that Radial’s technology stack incorporates and is optimized for using specific technologies
- Minimum of 4-5 years of big data experience with multiple programming languages and technologies with a minimum of two years of relevant ML experience
- Ability to manage less complex engagements and should have demonstrated risk management & project management skills
- Bachelor's degree or Master's degree from an accredited college/university in Computer Science, Computer Engineering, or related field PhD with minimum two years of big data and machine learning implementation experience
- Demonstrated ability to communicate complex technical concepts succinctly to non-technical colleagues, understand & manage interdependencies between all facets of a project
- Demonstrated ability to manage the team to drive engagements to successful closure and also the ability to mentor others
- Strong ability to rapidly ingest, transform, engineer, and visualize data for both ad-hoc and product-level (e.g., automated) data & analytics solutions
- Market-leading fluency in several programming languages such as Python, Scala, or Java, with the ability to pick up new languages and technologies quickly
- Understanding of cloud and distributed systems principles, including load balancing, networks, scaling, in-memory vs. disk, caching, high throughput with low latency solutions.
- Experience with large-scale big data methods such as MapReduce, Hadoop, Spark, Hive, Impala, or Storm Full-stack development capability is preferred
- Proficiency with programming methodologies such as version control, testing, QA, and development methodologies such as Waterfall and Agile
- Ability to work efficiently under Unix/Linux environment with experience with source code management systems like GIT Experience with object-oriented design, coding and testing patterns as well as experience in engineering (commercial or open source) software platforms and large-scale data infrastructures
- Familiarity with different architecture patterns of development such as Event Driven, SOA, micro services, functional programming, Lambda Capability to architect highly scalable distributed systems, using different open source tools.