What you'll do:

• Lead the migration of monolithic applications to microservices architecture.
• Manage the deployment and orchestration of services on Kubernetes (EKS).
• Optimize backend services to enhance performance and scalability.
• Focus on resolving core platform tech debts to ensure a robust and maintainable system.
• Design and manage data pipelines using tools like Apache Kafka, Flink, AWS Glue, and Airflow.
• Perform database tuning and optimization to ensure high performance and reliability.
• Optimize RDS/Redshift queries for better performance and efficiency.
• Utilize ELT tools like DBT for data transformation and pipeline management.
• Identify and implement strategies to reduce infrastructure and operational costs.
• Optimize resource allocation and utilization to achieve significant cost savings without compromising performance.
• Enhance platform security by working closely with DevSecOps teams.
• Implement best practices for data security, access controls, and compliance.
• Document SOPs for optimal local development setups and share best practices within the team.
• Set up development environments to streamline SDE workflows.
• Foster a culture of continuous improvement and efficiency within the engineering team.
• Develop a comprehensive health check system for services, including startup, liveness, and readiness probes.
• Monitor and alert on key metrics (e. g., API health, latency, error rates) using tools like Cube APM and PagerDuty.
• Ensure 100% log visibility and manage service-level health checks confidently.

What makes you a great fit:

• 2-5 years of experience as a Platform/Data Engineer at a product company with a decent scale.
• Demonstrated experience in big migration projects such as monolith to microservices, Kubernetes adoption, or cost-saving initiatives.
• Experience working closely with product engineering and DevSecOps teams.
• Proficient in Java and related frameworks.
• Strong experience with observability stacks and tools like OpenSearch, Kibana, Grafana, Prometheus, New Relic, Percona, AWS Cloudwatch, and RDS Performance Insights.
• Expertise in database tuning and optimization.
• Good to have: Experience with data engineering tools.
• Apache Kafka, Apache Flink, Amazon EKS, Amazon S3 AWS Glue, Amazon Athena, Presto, Apache Airflow, Apache Spark, Amazon EMR, Amazon OpenSearch, Kibana, SQL.
• Hands-on experience with creating and managing data pipelines using paid/open-source tools is a plus.
• Experience with ELT tools like DBT for data transformation.
• Proficiency in optimizing RDS/Redshift queries for performance.