Top Tech Jobs & Startup Jobs

Build and scale reliable cloud and on-premises infrastructure across global data centers and multiple platforms. Design Infrastructure as Code solutions, observability systems, deployment platforms, and internal tooling. Standardize infrastructure and configuration practices, participate in on-call and incident response, conduct root cause analysis, and improve long-term reliability. The role also involves mentoring engineers and partnering with Security, Networking, and Engineering teams to strengthen platform resilience.

Leads data initiatives across the Sportsbook Trading domain, defining priorities, roadmaps, requirements, and success measures. Coordinates Engineering, Analytics, Trading, and Data teams to deliver complex programs, manage dependencies, mitigate risks, and improve data capabilities. Identifies AI automation opportunities, supports production incident response, and develops technical program management frameworks. Requires strong SQL, data platform knowledge, technical communication, and experience in data-intensive environments such as Sportsbook, iGaming, or Trading.

Build and maintain full-stack features for real-time systems using event-driven microservices and microfrontend architectures. Responsibilities include writing tested code, participating in architecture discussions, troubleshooting with observability tools, supporting deployments and on-call workflows, and collaborating with senior engineers to deliver reliable customer-facing and internal applications.

Own database reliability, scalability, and operational excellence across cloud and on-premises environments. Build automated Kubernetes-based database platforms, infrastructure tooling, failover and backup systems, and self-healing capabilities. Lead observability, performance optimization, capacity planning, incident response, and safe migration practices. Partner with application teams, leverage AI for operational improvements, mentor engineers, influence architecture, and drive technical strategy for highly available database infrastructure.

Leads data scientists and machine learning engineers developing personalized customer experiences. Sets strategy, roadmaps, and priorities; partners with Product, Engineering, Marketing, Revenue, and Data Science leaders; oversees machine learning systems from design through deployment, monitoring, and optimization; advances MLOps, software engineering, experimentation, and production reliability practices; and manages capacity, resources, technical standards, and team development.

Build and scale reliable backend systems and APIs for high-traffic, real-time sports betting applications. Responsibilities include writing production code, debugging and resolving software issues, developing automated tests, collaborating with cross-functional teams, and delivering scalable features using modern engineering practices.

Own technology and product procurement category strategies, lead complex sourcing initiatives, negotiate sophisticated technology agreements, and manage strategic supplier relationships. Advise senior stakeholders on commercial models, supplier selection, market dynamics, and build-versus-buy decisions. Use spend analytics, benchmarking, financial modeling, automation, and AI to optimize total cost of ownership, manage risk, deliver enterprise value, and advance procurement capabilities.

Leads product design for complex platform capabilities and strategic initiatives. Defines product vision, creates scalable experiences and reusable design systems, partners with cross-functional teams and Engineering, influences product strategy, sets quality standards, provides critique, and mentors designers. Requires strong interaction design, systems thinking, communication, storytelling, and experience delivering measurable outcomes across shared platforms, internal tools, enterprise workflows, or multi-user products.

Develop and maintain native iOS applications using Swift and SwiftUI for DraftKings’ financial platform. Collaborate with product, design, analytics, and engineering teams to build features, integrate APIs, manage data, optimize performance, monitor reliability and crash analytics, and troubleshoot issues. Participate in code reviews, testing, and mobile release processes while delivering scalable, compliant, user-focused experiences.

Build, scale, and operate Databricks and AWS infrastructure supporting automated, reproducible machine learning workflows. The role includes infrastructure-as-code with Terraform or Pulumi, containerized platforms using Docker and Kubernetes, system architecture, reliability and cost optimization, and cross-functional collaboration with data and ML teams. The engineer will lead complex projects, improve platform scalability and usability, document systems, and mentor peers while operating production-scale data and ML platforms.