Staff Data Platform Engineer

10+ years of industry experience with a focus on backend and data engineering. Proven experience designing and operating distributed data systems using a microservices architecture, with a solid understanding of scalability, fault tolerance, performance, and observability best practices. Hands-on experience with CI/CD pipelines and deployment automation. A track record of writing high-quality, maintainable code across multiple services and teams. Strong leadership skills to guide, mentor, and support engineers while fostering a collaborative team culture. Excellent communication, analytical, and problem-solving skills. Ability to adapt to a rapidly changing environment with evolving or ambiguous requirements. Demonstrated ability to work independently, take ownership of complex projects, and drive them to completion with minimal supervision. Ability to leverage AI-powered tools and frameworks to enhance developer productivity, automate workflows, and optimize data-driven systems. Experience with AWS serverless technologies (e.g., SST, CDK, Terraform), NoSQL databases (e.g., DynamoDB), stream processing (e.g., Kinesis, DynamoDB Streams). Experience developing backend services using modern languages such as TypeScript and Node.js. Experience deploying and managing applications on public cloud platforms (e.g., AWS, GCP).

Lead the architecture and design of complex, large-scale distributed systems. Design, develop, and maintain scalable, fault-tolerant microservices for both real-time and batch data processing. Build and maintain high-performance REST APIs with a focus on scalability, reliability, and maintainability. Design and implement data models and queries for efficient storage and retrieval across SQL and NoSQL databases. Lead data quality initiatives to ensure consistency, accuracy, and reliability of data across the platform. Collaborate with cross-functional teams to translate platform requirements into effective technical solutions. Drive technical standards and best practices for code quality, testing, and observability across engineering teams. Mentor and provide technical guidance to engineers through code reviews, design discussions, and direct support. Take end-to-end ownership of the software development lifecycle, including deployment, monitoring, and incident response.