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Data Architect, Professional Services
Há 1 mês
- Relevant experience with Data platform implementation
- Relevant hands-on experience in implementation and performance tuning of Kinesis, Kafka, Spark or similar implementations
- Hands on experience with building data or machine learning pipeline
- Experience with one or more relevant tools (Flink, Spark, Kafka, Amazon Kinesis)
- Experience developing software code in one or more programming languages (Java, Scala, Python, etc)
- Ability to communicate fluently in Portuguese and English, in technical and business settings (verbal and written).
Job summary
Are you a Data Analytics specialist? Do you have Data Warehousing, Hadoop/Data Lake experience? Do you like to solve the most complex and high scale (billions + records) data challenges in the world today? Do you like to work on-site in a variety of business environments, leading teams through high impact projects that use the newest data analytic technologies? Would you like a career path that enables you to progress with the rapid adoption of cloud computing?
In this role, you will work with our partners, customers and focus on our AWS 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.
Job responsibilities
Expertise - Collaborate with AWS field sales, pre-sales, training and support teams to help partners and customers learn and use AWS services such as Athena, Glue, Lambda, S3, DynamoDB NoSQL, Relational Database Service (RDS), Amazon EMR and Amazon Redshift.
Solutions - Deliver on-site technical engagements with partners and customers. This includes participating in pre-sales on-site visits, understanding customer requirements, creating packaged Data & Analytics service offerings.
Insights - Work with AWS engineering and support teams to convey partner and customer needs and feedback as input to technology roadmaps. Share real world implementations and recommend new capabilities that would simplify adoption and drive greater value from use of AWS cloud services.
Innovate - Engaging with the customer's business and technology stakeholders to create a compelling vision of a data-driven enterprise in their environment
About Us
Inclusive Team Culture
Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 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.
Work/Life Balance
Our team puts a high value on work-life balance. It isn't about how many hours you spend at home or at work; it's about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.
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.
- BS in Computer Science, Physics, Engineering or Math.
- Familiar with Machine learning concepts and algorithms
- Hands on experience working on large-scale data science/data analytics projects
- Experience Implementing Cloud services in a variety of distributed computing, enterprise environments.
- Experience with at least one of the modern distributed Machine Learning and Deep Learning frameworks such as TensorFlow, PyTorch, MxNet, 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.
- Relevant experience developing cloud software services and an understanding of design for scalability, performance and reliability.
- Experience with Cloud technology stacks.
- 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.
