Staff Data/Database Platform Engineer – Postgres

Staff Data/Database Platform Engineer – Postgres

Requirements

• Deep expertise in data and storage technologies, including RDBMS (e.g., Postgres), NoSQL, and other database types (e.g., columnar, document, key-value, and unstructured), with a strong understanding of tradeoffs and use cases for each.
• Demonstrated experience with advanced database scaling strategies for relational systems.
• Strong knowledge of high-availability architectures and proficiency with monitoring tools to support uptime and incident response.
• Experience with cloud-based database and data processing platforms, such as Amazon Aurora, Databricks, AWS RDS, Redshift, BigQuery, Snowflake, and managed services like AWS EMR and Google Cloud Dataflow.
• Familiarity with message queues, ETL workflows, and data pipelines for real-time and batch processing.
• Strong programming skills (e.g., Python, Bash, SQL) and experience with CI/CD practices.
• Experience in an on-call rotation and handling incident response.
• Excellent communication and collaboration skills, with a proven ability to work effectively with data and product engineering teams.

Responsibilities

• Database Scaling and Optimization: Design and implement scaling strategies for relational systems to ensure they meet the high availability and scalability needs of data and product engineering teams.
• Availability and Uptime Management: Proactively monitor and optimize database systems to meet stringent uptime requirements. Participate in an on-call rotation to respond to incidents, troubleshoot issues, and restore service promptly during disruptions.
• Architect and Optimize Database Infrastructure: Manage a variety of database technologies, balancing tradeoffs across relational, columnar, document, key-value, and unstructured data solutions, providing a foundation for data warehousing and supporting data-driven product needs.
• Integration with Data Engineering and Product Pipelines: Collaborate with data and product engineering teams to implement and optimize data pipelines, including message queues (e.g., Kafka), ETL workflows, and real-time processing, ensuring efficient and reliable data movement.
• Infrastructure Automation and Reliability: Utilize infrastructure as code (IaC) to automate deployment, scaling, and maintenance, creating a consistent, reliable environment that supports high availability and deployment efficiency for both data and product teams.
• Performance Tuning and Incident Response: Conduct performance tuning, establish monitoring and alerting, and address potential issues quickly to ensure a responsive platform that meets the needs of all engineering workloads.
• Documentation and Knowledge Sharing: Document processes, including scaling strategies, monitoring setups, and best practices, to support alignment with engineering requirements and ensure smooth handoffs in on-call situations.

Preferred Qualifications

No preferred qualifications provided.