Senior Lead Database Reliability Engineer

DraftKings Inc.

1d•$168,000 - $210,000

About The Position

At DraftKings, AI is becoming an integral part of both our present and future, powering how work gets done today, guiding smarter decisions, and sparking bold ideas. It’s transforming how we enhance customer experiences, streamline operations, and unlock new possibilities. Our teams are energized by innovation and readily embrace emerging technology. We’re not waiting for the future to arrive. We’re shaping it, one bold step at a time. To those who see AI as a driver of progress, come build the future together. The Crown Is Yours As a Senior Lead Database Reliability Engineer, you'll own the reliability, scalability, and operational excellence of the database infrastructure powering one of the most demanding real time platforms in sports betting and gaming. In this role, you'll combine deep database expertise with software engineering and infrastructure automation to build resilient, self healing systems, improve platform performance, and drive reliability across cloud and on premises database environments.

Requirements

At least 6 years of experience in Database Reliability Engineering, Database Platform Engineering, or Site Reliability Engineering with a strong database focus, including technical leadership experience delivering complex database infrastructure projects at scale.
Deep expertise in at least one major relational database, preferably PostgreSQL, along with operational experience supporting technologies such as MySQL, MongoDB, Redis, ScyllaDB, Aerospike, Aurora, Cloud SQL, and other managed cloud database services.
Strong experience building and operating stateful workloads on Kubernetes using technologies such as StatefulSets, Persistent Volumes, database operators, Terraform, Pulumi, FluxCD, ArgoCD, GKE, and EKS.
Hands on software development experience using Go or Python to create automation, platform tooling, Kubernetes controllers, APIs, and infrastructure that reduces manual effort and improves engineering efficiency.
A data driven, automation first mindset with proven experience improving reliability through observability, monitoring, service level objectives, capacity planning, performance optimization, and self service engineering solutions.
Practical experience using AI tools such as Claude, GitHub Copilot, Cursor, MCP, or similar technologies to improve design, coding, documentation, troubleshooting, and operational workflows while applying sound engineering judgment to validate AI generated outputs.
Excellent leadership and communication skills with a track record of mentoring engineers, influencing architectural decisions, collaborating across engineering teams, producing clear technical documentation, and driving continuous improvement in highly available production environments.

Responsibilities

Drive the technical roadmap for database reliability across PostgreSQL, MySQL, MongoDB, Redis, ScyllaDB, Aerospike, and managed cloud services, while shaping architecture for high availability, replication, partitioning, storage, and connection management.
Design and build automation first database platforms by developing Kubernetes operators, infrastructure as code, GitOps workflows, and production quality tooling in Go or Python to automate provisioning, failover, backups, schema migrations, and lifecycle management.
Lead operational excellence by defining service level objectives, monitoring database health, capacity, and performance, eliminating recurring reliability issues, validating backup and recovery processes, and leading critical production incidents through resolution and continuous improvement.
Optimize database performance and cost across cloud and on premises environments by driving capacity planning, resource efficiency, storage optimization, workload consolidation, and performance tuning for large scale systems.
Partner closely with application engineering teams to establish safe database practices, including schema reviews, migration strategies, query optimization, connection management, and zero downtime deployment processes.
Leverage AI to improve engineering productivity and database operations through intelligent observability, anomaly detection, root cause analysis, documentation, predictive insights, and evaluation of AI generated code to ensure reliability and security.
Mentor engineers across the organization by sharing best practices, leading design and code reviews, influencing technical direction, supporting hiring efforts, and raising the overall maturity of database reliability engineering.