Sr. Software Development Engineer, RBKS AI Data Management

• Experience as a full-stack software engineer, with a track record of delivering highly scalable, mission-critical data systems
• Proficiency in designing and building distributed, fault-tolerant data pipelines and batch/streaming data processing workflows
• Strong programming skills in languages like Java, Python, Scala, or Golang, with a deep understanding of software engineering best practices
• Hands-on experience with modern data storage and processing technologies, such as Kafka, Spark, Hadoop, Cassandra, Redshift, or BigQuery
• Familiarity with cloud computing platforms (e.g., AWS, Azure, GCP) and infrastructure-as-code tools (e.g., Terraform, CloudFormation)
• Expertise in developing and implementing robust monitoring, alerting, and automated remediation for data systems
• Experience with agile software development methodologies and tools (e.g., Jira, GitHub, CI/CD pipelines)
• Strong analytical and problem-solving skills, with the ability to tackle ambiguous, complex challenges
• Excellent verbal and written communication skills to effectively collaborate with cross-functional teams
• Bachelor’s degree in Computer Science, Software Engineering, or a related field.
• Experience designing and building large-scale data platforms to support machine learning and artificial intelligence use cases
• Track record of delivering complex, cross-functional data projects that drive measurable business impact
• Familiarity with data engineering best practices around data quality, lineage, governance, and lifecycle management
• Understanding of machine learning data workflows, including data collection, annotation, feature engineering, and model training/deployment
• Knowledge of data privacy and security frameworks (e.g., GDPR, CCPA) and experience implementing solutions to address compliance requirements
• Familiarity with low-code/no-code development tools and their application in data automation and self-service capabilities
• Proven ability to mentor and lead more junior engineers, fostering their technical growth and career development
• Experience in delivering presentations and technical roadmaps to executive-level stakeholders
• Background in applied research or academic work involving large-scale data processing and analysis
• Master’s degree in Computer Science, Software Engineering, or a related field

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The Ring, Blink, Key and Sidewalk (RBKS) AI Data Management team is responsible for ensuring efficient data annotation, delivery, and capacity planning to support our growing AI initiatives. As we continue to scale, there is a critical need to automate manual processes, streamline data delivery, and optimize resource allocation. To help drive these transformative changes, we are seeking an experienced Senior Software Development Engineer to lead the design and implementation of innovative, scalable data solutions.

In this role, you will work across multiple SDE organizations, Applied Science teams, and partner groups to build flexible, resilient data pipelines that enable our researchers to access high-quality training data just-in-time. Your technical leadership and strategic vision will be instrumental in reducing manual annotation efforts, improving data delivery turnaround times, and enhancing our capacity planning capabilities.

As an Sr SDE, you will excel at solving ambiguous, complex problems and proactively mitigating risks before they become roadblocks. You will be a highly autonomous, data-driven engineer, regularly reviewing metrics and experimenting with new approaches to increase the reliability and efficiency of our data workflows. Importantly, you will also ensure your technical work remains tightly aligned with the organization's overarching AI and data management strategy.

This role requires exceptional technical skills, strong business acumen, and the ability to influence and build consensus across diverse, cross-functional teams. Your primary focus will be on delivering innovative data solutions while simultaneously improving the long-term flexibility and maintainability of our data ecosystem.

Key job responsibilities
Tactical:
• Conduct a comprehensive analysis of existing annotation workflows and develop/implement automation tools to streamline manual tasks
• Design and deploy scalable, fault-tolerant data collection, annotation, and delivery pipelines to support the growing needs of our Applied Science teams
• Collaborate with SDE, Data Engineering, and Applied Science teams to define requirements and ensure seamless integration of data solutions
• Automate data workflows and build reusable, self-service capabilities to increase the speed and agility of our data delivery
• Proactively identify and mitigate technical risks, demonstrating solid judgment in determining when to escalate
• Foster a culture of testing, monitoring, and continuous improvement across data systems

Strategic:
• Develop the long-term technical strategy and roadmap for the Data Management ecosystem, aligning with the organization's vision of delivering data for AI models with minimal manual intervention
• Assess emerging technologies and data management trends, and evaluate their potential impact on our data architecture and delivery capabilities
• Decompose ambiguous, complex problems into simplified, scalable solutions that reduce friction and improve flexibility
• Communicate technical designs, trade-offs, and outcomes effectively to senior leadership (Director level and above)
• Foster consensus and alignment across teams to drive coherent, enterprise-wide approaches to data challenges
• Actively mentor and develop more junior SDEs within the organization

A day in the life
As an experienced software engineer, you're looking for a role that combines technical excellence with strategic impact. That's exactly what you'll find as the Sr SDE leading the data pipeline efforts for Ring's AI Data Management team.

Your day starts with reviewing the latest metrics and performance trends across our data collection, annotation, and delivery workflows. This data-driven approach keeps you closely connected to the real-world outcomes of your work. For example, you notice a concerning uptick in data delivery latency for a key ML model - a problem you're eager to investigate and resolve.

Next, you huddle with your Data Engineering and Applied Science counterparts. These collaborative working sessions are where the magic happens. Together, you map out the end-to-end data lifecycle, identify integration points and potential failure modes, and explore innovative ideas for streamlining the architecture. Your big-picture, systems-level thinking is crucial as you work to enhance the flexibility and reliability of our data ecosystem.

In the afternoon, you may transition to a more tactical initiative - like finalizing the designs and implementation plan for a new data annotation automation tool. This is where your exceptional software engineering skills really shine. You work closely with the Annotation and Quality Assurance teams, ensuring the solution not only addresses their most pressing challenges, but also delivers an intuitive, scalable user experience. You thrive on creating impactful, long-lasting technical solutions.

Of course, your day is never entirely predictable. You're likely to field ad-hoc requests and escalations from various teams throughout the day. A Data Scientist may need your help accessing a critical dataset, or an Engineering lead may be blocked on a tricky cross-team dependency. Your ability to rapidly triage, identify root causes, and propose viable resolutions is a key part of your value.

As an Sr SDE, you also make time to mentor more junior engineers on the team. This could involve reviewing their code, providing feedback on technical design choices, or discussing strategies for navigating ambiguity. Developing the next generation of data platform leaders is immensely rewarding work.

Finally, you carve out time in the late afternoon to revisit your long-term technical roadmap. You know that truly transformative change requires a thoughtful, well-communicated vision. So you reflect on the latest inputs, assess your progress against key goals (like that 10% annotation effort reduction), and start outlining the next evolution of your data architecture strategy to share with senior leadership.

No two days are exactly the same, but this captures the essence of what you'll experience as the Sr SDE for AI Data Management. It's a role that combines your technical mastery with the opportunity to drive strategic, high-impact change.