What you will work on:

• Define IAM Domain Data Schema and propose efficient, scalable data schemas tailored to the IAM domain, understanding current IAM systems, identifying gaps, and recommending improvements in data structure.
• Establish data governance and ownership by setting clear roles and responsibilities for data management, defining data quality metrics, and ensuring compliance with internal and external data policies. Create comprehensive documentation outlining best practices for data modeling specific to the IAM domain.
• Collaborate with engineering teams on event instrumentation, ensuring accurate and actionable data collection by setting instrumentation requirements.
• Work in multi-functional agile teams with end-to-end responsibility for product development and delivery within the organization.
• Explore innovative methods for producing, processing, and analyzing data to unlock insights into users and product features.
• Design, develop, and test data-driven products and features while utilizing state-of-the-art data processing frameworks, technologies, and platforms.
• Improve data quality through testing, tooling, and continuous performance evaluation.
• Collaborate with software engineers, data scientists, and decision-makers, including engineering and product managers, to build solutions and gain novel insights.
• Act as a bridge between the Engineering and Insights teams, focusing on data cataloging/management and building/maintaining crucial data pipelines.

What you bring:

• 3+ years of hands-on experience in developing data products using Scala, Python, and SQL.
• 3+ years of experience in architecture and design patterns for complex systems, focusing on reliability, scalability, and quality.
• Advanced coding skills and practices, including concurrency, distributed systems, functional principles, and performance optimization.
• Experience in operating and maintaining production-grade software.
• Strong knowledge of software engineering standard methodologies.
• Experience performing analysis with large datasets in a cloud-based environment, preferably with an understanding of Google Cloud Platform.
• Proficiency in JVM-based data processing frameworks such as Beam, Spark, and Flink, with a solid understanding of their APIs and debugging capabilities.
• Proven understanding of data modeling, data access, and data storage, including caching, replication, and optimization techniques.
• Appreciation for collaborative teamwork, with the ability to communicate asynchronously and work independently while sharing context with team members.
• Experience with backend engineering is a plus.