Senior Data Platform Engineer

About this role

About the Company

Gemini is a global crypto and Web3 platform founded by Cameron and Tyler Winklevoss in 2014, offering a wide range of simple, reliable, and secure crypto products and services to individuals and institutions in over 70 countries. Our mission is to unlock the next era of financial, creative, and personal freedom by providing trusted access to the decentralized future. We envision a world where crypto reshapes the global financial system, internet, and money to create greater choice, independence, and opportunity for all — bridging traditional finance with the emerging cryptoeconomy in a way that is more open, fair, and secure. As a publicly traded company, Gemini is poised to accelerate this vision with greater scale, reach, and impact.

The Department: Platform

Our Platform organization’s purpose is to enable Gemini to scale effectively and empower our engineering teams to focus on building innovative financial products and experiences for individuals around the world. Platform focuses around building a scalable and secure foundations platform, enabling Engineering to deploy, validate, and operate their services in production, improve resiliency of the service and increase organizational efficiency by reducing operational toil and increase system efficiency through architectural evolution.

The Platform team engages directly with our other engineering teams to onboard them onto our platform systems, reviewing and recommending design and architectural decisions, and guiding our engineering teams on how to implement the tooling provided by the larger Platform organization required to ensure systems can scale and react to changing conditions, with continuous improvement loops.

The Role: Senior Data Platform Engineer

As a Senior Data Platform Engineer, you'll be an SRE for our data infrastructure: responsible for the reliability, automation, and operability of the database and datastore fleet, not just tuning any single engine. You'll work closely with data engineering and product engineering teams to build self-service, automated foundations that let teams provision, scale, and operate their own datastores safely.

Your role centers on deep relational database expertise (PostgreSQL, Aurora) - internals, replication, failover, backup/recovery, and query performance - paired with the automation and tooling to run that expertise at scale rather than by hand. You'll extend that same automation-first discipline to the other datastores in our fleet - NoSQL/document, columnar, key-value, and streaming systems - so that scaling, failover, and provisioning are repeatable and largely self-service. You'll drive improvements to observability, incident response, and uptime posture through proactive ownership of the platform, and you'll bring SRE practices (SLOs, error budgets, toil reduction, blameless postmortems) to how we run data infrastructure.

This role is ideal for someone who thinks in systems and automation first, thrives on cross-team collaboration, and wants to reduce operational toil across a diverse set of database technologies in a fast-paced, cloud-native environment.

Responsibilities:

• Automation and Reliability Engineering: 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.

• Database Scaling and Optimization: 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.

• Fleet-Wide Infrastructure Design: 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.

• High Availability, Observability, and SRE Practice: 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.

• Pipeline Integration: 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.

• Performance Tuning and Troubleshooting: 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.

• Toil Reduction and Operational Excellence: 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.

Qualifications:

• 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

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