What you will do

• Enable teams across the company to get value from data using our internal tools and platform. You'll work with engineers, analysts, and stakeholders across. You'll work with engineers, analysts, and stakeholders across different tech stacks and business domains - for a platform team. Communicating well is as important as the code we write.
• Set up and govern Amazon SageMaker Unified Studio (SMUS) for other teams, so they can work independently without us becoming a bottleneck.
• Keep our AWS data infrastructure available, scalable, and secure, and own the operational side of what you build.
• Build and optimize event-driven ingestion pipelines with Amazon Kinesis, Amazon Data Firehose and AWS Lambda.
• Help the team grow.
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What will help you to fulfill your role

• You want to work as a team, not just alone: pair programming, sharing knowledge, mentoring and clear communication should feel natural in your day-to-day work rather than overhead.
• Strong programming skills in Python. Experience with the JavaScript/TypeScript ecosystem and/or a JVM language is a plus.
• Experience developing and running applications in cloud-native environments (for example AWS Lambda and S3), deployed through Infrastructure as Code with CDK or Terraform.
• A solid DevOps foundation: CI/CD, containers, monitoring and alerting.
• Working knowledge of table and data formats such as Apache Iceberg, Parquet, Protocol Buffers.
• Hands-on experience applying AI in a data context: to speed up your own development, to automate routine work, and to help others do the same.
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Nice-to-Haves:

• Exposure to data governance, GDPR and data privacy engineering.
• Experience with SQL in both analytical and transactional databases.
• Familiarity with orchestration frameworks for data pipelines, such as Apache Airflow.
• Experience designing and building (data) APIs.
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