Gemini is a global crypto and Web3 platform offering a wide range of crypto products and services. The Senior Data Platform Engineer will be responsible for the reliability, automation, and operability of the database and datastore fleet, working closely with data engineering and product engineering teams to build self-service, automated foundations.

Responsibilities:

Build Infrastructure as Code (IaC), CLI tools, and CI/CD-driven automation that make database provisioning, scaling, failover, and deployment self-service, consistent, and repeatable across environments - this is the core of the role, not a supporting activity
Serve as the team's depth on relational database systems (e.g., Amazon Aurora, PostgreSQL) - replication topologies, failover, backup/recovery, and query/engine-level performance - ensuring high performance and availability under growing workloads
Extend that operational rigor to the rest of the datastore fleet - document, key-value, and columnar systems - applying the right paradigm to the right workload and building common tooling and guardrails across all of them
Define and track SLOs/error budgets, build proactive monitoring and alerting, implement high-availability architectures, and participate in the on-call rotation to troubleshoot and resolve production issues quickly
Collaborate with data and product engineering teams to integrate with upstream and downstream pipelines - both real-time and batch - via message queues (e.g., Kafka), ETL workflows, and processing frameworks
Identify and resolve performance bottlenecks at both the query and infrastructure levels across engines. Establish alerting, observability, and incident response procedures that reduce MTTR and maintain service health
Continuously identify and automate away repetitive operational work; contribute to shared documentation, incident retrospectives, and platform playbooks to improve team effectiveness and reliability of operations

Requirements:

5 years of experience in the field
Deep, specialist-level experience managing and scaling relational databases - cloud-native systems like PostgreSQL, Amazon Aurora, or similar - including replication, failover, backup/recovery, and query/engine performance tuning
Demonstrated SRE mindset: experience building automation, self-service tooling, and guardrails that eliminate manual, repetitive database operations rather than performing them by hand
Hands-on experience with at least one non-relational paradigm in production (e.g., NoSQL, columnar, document, key-value), and working knowledge of when to apply each
Familiarity with cloud-based data platforms and services such as AWS RDS, Redshift, EMR, Google BigQuery, or Databricks
Experience in an infrastructure as code environment (Terraform), developing automated solutions to solve support and operational issues
Proficiency writing scripts, CLIs, or services that increase developer productivity and reduce operational toil, in languages like Python, Go, etc
Understanding of CI/CD, observability tooling, SLOs/error budgets, and incident response in production environments
Experience integrating with data pipelines and real-time messaging systems like Kafka or Kinesis
Comfortable participating in on-call rotations and owning uptime and recovery responsibilities across multiple database technologies
Strong communication and collaboration skills; able to work effectively across infrastructure, data, and product teams

Kubernetes Platform Engineer

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