At Amazon Web Services (AWS), we're hiring highly technical cloud computing architects and engineers to collaborate with our customers and partners on key engagements. Our consultants will develop and deliver proof-of-concept projects, technical workshops, and support implementation projects. These professional services engagements will focus on customer solutions such as Machine Learning, Data and Analytics, HPC and more.
In this role, you will work with our partners, customers and focus on our AWS Analytics and ML service offerings such Amazon Kinesis, AWS Glue, Amazon Redshift, Amazon EMR, Amazon Athena, Amazon SageMaker and more. You will help our customers and partners to remove the constraints that prevent them from leveraging their data to develop business insights.
AWS Professional Services engage in a wide variety of projects for customers and partners, providing collective experience from across the AWS customer base and are obsessed about customer success. Our team collaborates across the entire AWS organization to bring access to product and service teams, to get the right solution delivered and drive feature innovation based upon customer needs.
In our Global Specialist Practice, you will also have the opportunity to create white papers, writing blogs, build demos and other reusable collateral that can be used by our customers, and, most importantly, you will work closely with our Solution Architects, Data Scientists and Service Engineering teams.
The ideal candidate will have extensive experience with design, development and operations that leverages deep knowledge in the use of services like Amazon Kinesis, Apache Kafka, Apache Spark, Amazon Sagemaker, Amazon EMR, NoSQL technologies and other 3rd parties.
Excellent business and communication skills are a must to develop and define key business questions and to build data sets that answer those questions. You should be able to work with business customers in understanding the business requirements and implementing solutions.
Inclusive Team Culture
Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 85,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon's culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
Our team puts a high value on work-life harmony. Striking a healthy balance between your personal and professional life is crucial to your happiness and success here. We are a customer-obsessed organizationleaders start with the customer and work backwards. They work vigorously to earn and keep customer trust. As such, this is a customer facing role in a hybrid delivery model. Project engagements include remote delivery methods and onsite engagement that will include travel to customer locations as needed.
Mentorship & Career Growth
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we're building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded professional and enable them to take on more complex tasks in the future.
This is a customer facing role. You will be required to travel to client locations and deliver professional services when needed.
• Bachelor's degree, or equivalent experience, in Computer Science, Engineering, Mathematics or a related field
• 5+ years' experience of Data platform implementation, including 3+ years of hands-on experience in implementation and performance tuning Kinesis/Kafka/Spark/Storm implementations.
• Experience with analytic solutions applied to the Marketing or Risk needs of enterprises
• Basic understanding of machine learning fundamentals.
• Ability to take Machine Learning models and implement them as part of data pipeline
• 5+ years of IT platform implementation experience.
• Experience with one or more relevant tools ( Flink, Spark, Sqoop, Flume, Kafka, Amazon Kinesis ).
• Current hands-on implementation experience required
• Masters or PhD in Computer Science, Physics, Engineering or Math.
• Hands on experience working on large-scale data science/data analytics projects.
• Ability to lead effectively across organizations.
• Hands-on experience with Data Analytics technologies such as AWS, Hadoop, Spark, Spark SQL, MLib or Storm/Samza.
• Implementing AWS services in a variety of distributed computing, enterprise environments.
• Proficiency with at least one the languages such as C++, Java, Scala or Python.
• Experience with at least one of the modern distributed Machine Learning and Deep Learning frameworks such as TensorFlow, PyTorch, MxNet Caffe, and Keras.
• Experience building large-scale machine-learning infrastructure that have been successfully delivered to customers.
• Experience defining system architectures and exploring technical feasibility trade-offs.
• 3+ years experiences developing cloud software services and an understanding of design for scalability, performance and reliability.
• Experience working on a code base with many contributors.
• Ability to prototype and evaluate applications and interaction methodologies.
• Experience with AWS technology stack.
• Written and verbal technical communication skills with an ability to present complex technical information in a clear and concise manner to a variety of audiences.
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us .
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