Amazon Software Development Engineer RBKS AI Data Management 

Poland, Pomeranian Voivodeship, Gdansk 

418719070

The Ring, Blink, Key and Sidewalks (RBKS) AI Data Management team is seeking an experienced L5 Software Development Engineer to deliver innovative solutions that improve the reliability, efficiency, and scalability of our data pipelines. In this role, you will work closely with our Data Engineering, Applied Science, and Operations teams to design, implement, and maintain the critical systems that enable our researchers to access high-quality training data.As an SDE, you will excel at taking ambiguous, complex problems and developing thoughtful, well-architected solutions. You will leverage your strong technical skills and business acumen to drive cross-functional collaboration, ensure seamless integration of data capabilities, and continuously optimize our data workflows.
This position requires a hands-on, iterative approach to software engineering. You will be responsible for the full lifecycle of data-centric projects - from requirements gathering to deployment and maintenance.Key job responsibilities
• Design, develop, and deploy scalable, fault-tolerant data collection, annotation, and delivery pipelines
• Collaborate with Data Engineering, Applied Science, and Operations teams to understand requirements, identify risks, and ensure smooth integration of data solutions
• Automate manual data workflows and build reusable, self-service capabilities to increase speed and agility of data delivery
• Proactively monitor data system health, investigate issues, and drive continuous improvements to increase reliability
• Communicate technical designs, trade-offs, and outcomes effectively to cross-functional stakeholders
• Stay up-to-date on the latest data management trends and technologies, and evaluate their applicability to our ecosystemA day in the life
You might start your morning by reviewing the latest performance metrics and monitoring dashboards for our data collection, annotation, and delivery pipelines. This data-driven approach allows you to quickly identify any issues or emerging bottlenecks that require your attention. For example, you notice a concerning spike in data processing latency, which could signal an underlying systems problem.Next, you jump into a working session with your Data Engineering and Applied Science counterparts. Together, you dig into the root cause of the latency issue, exploring potential architectural changes or automation opportunities that could help resolve it. Your ability to rapidly context-switch between high-level design and low-level implementation is critical.In the afternoon, you may shift gears to a more proactive initiative - like finalizing the technical requirements and roadmap for a new self-service data annotation platform. This involves aligning with the Annotation team, understanding their pain points, and then translating those into a scalable, user-friendly software solution. You take an iterative, user-centric approach, gathering feedback and incorporating it throughout the development process.In the late afternoon, you may carve out time to research the latest data management trends and technologies. As the landscape rapidly evolves, you know it's critical to maintain your technical fluency and identify opportunities to enhance our data ecosystem. You jot down some ideas to discuss with your manager during your next 1-on-1.No two days are exactly the same, but this captures the essence of what you'll experience as an SDE in the AI Data Management team. It's a role that combines your deep technical expertise with the ability to drive cross-functional collaboration and continuous improvement.

• Experience as a full-stack software engineer, with a focus on building large-scale, distributed data systems
• Proficiency in designing and developing fault-tolerant, scalable data pipelines using technologies like Kafka, Spark, Hadoop, Cassandra, etc.
• Strong programming skills in languages like Java, Python, Scala, or Golang, with a deep understanding of software engineering best practices
• Hands-on experience with cloud computing platforms (e.g., AWS, Azure, GCP) and infrastructure-as-code tools
• Expertise in developing and implementing robust monitoring, alerting, and automated remediation for data systems
• Excellent problem-solving, analytical, and strategic thinking skills to tackle ambiguous challenges
• Outstanding verbal and written communication abilities to collaborate effectively with cross-functional teams
•Bachelor’s degree in Computer Science, Software Engineering, or a related field.

• Experience in applied AI/ML, data annotation workflows, or leading technical initiatives is highly preferred
• Master’s degree in Computer Science, Software Engineering, or a related field.