Software Architect

DraftKings Inc.

20h•$185,400 - $231,800•Onsite

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 Software Architect, you'll help design and scale the systems that power world-class digital experiences. You'll play a critical role in shaping architectural decisions while staying close to the code, ensuring our platforms are resilient, performant, and built to evolve. Working across Engineering, Product, Infrastructure, and Platform teams, you'll influence how we approach distributed systems at scale and elevate technical standards across the organization. Your work will strengthen our foundation today while enabling us to move faster and smarter tomorrow. DKExchange is DraftKings' regulated prediction markets exchange. In this role you'll take technical ownership of the domain - market data, order entry and matching, request-for-quote trading, clearing and settlement. You'll shape how the platform scales into peak sporting events, how quickly we can add new markets and partners, and how a fast, externally-facing trading system stays correct and available under heavy load. You'll also work with AI as a core part of how you deliver. We expect our architects to build and own the AI tooling for their domain - agents and automated workflows that handle the repetitive analysis, monitoring and drafting work - so your own time goes to the judgment calls. You'll be hands-on with it, you'll help your teams get good at it, and you'll stay accountable for the quality of what comes out.

Requirements

Professional software engineering experience, including significant experience designing and evolving large-scale distributed systems in production.
Experience with fast, high-volume transactional or event-driven systems where getting the order and the outcome exactly right matters as much as speed. Background in exchanges, trading, payments or other financial systems is a strong advantage.
Extensive knowledge of messaging and streaming systems; hands-on expertise with Kafka is a strong advantage.
A solid grasp of how to manage state across a distributed system - how to split and shard data, when to keep it in memory versus a durable store, and the consistency trade-offs involved.
Proven understanding of microservices architecture, including containerization and orchestration. Our services are mostly Go and .NET/C#, so comfort working across more than one language stack matters.
Familiarity with the protocols and contract-first design this domain runs on - FIX, gRPC and Protobuf - and experience integrating with third-party systems you don't control directly.
Working knowledge of relational and NoSQL databases, tradeoffs and advantages based on the use case.
Proficiency with modern development practices, including continuous integration and delivery, infrastructure as code, and observability tooling; comfort running load tests and building capacity projections.
Real, practical experience using AI tools on production work - coding assistants, agents, automated review and analysis - not just experimentation.
A practical view of the economics - able to weigh an AI-based approach against building or buying as part of normal technical decision-making.
Exposure to building software in a regulated environment: auditability, data sensitivity, and designing for compliance.
Clear, structured communication, with the ability to document decisions and influence both technical and non-technical audiences, including internal and external engineering teams, and other stakeholders.
A genuine interest in mentoring engineers, giving useful design feedback, and continuously raising engineering standards.

Nice To Haves

Background in exchanges, trading, payments or other financial systems is a strong advantage.
hands-on expertise with Kafka is a strong advantage.
comfort working across more than one language stack matters.
FIX, gRPC and Protobuf

Responsibilities

Define and evolve architectural solutions that improve system performance, scalability, reliability, and day-to-day operations.
Own the technical roadmap for the exchange, and produce the architecture and design for every major initiative in the domain.
Maintain the shared foundations - the core patterns, system boundaries, and data contracts that every team builds on, documented clearly enough that both people and AI tools can work from them.
Drive consistency in the technologies and patterns used across the exchange's services.
Partner closely with Product, Infrastructure, Platform and partner engineering teams - including brokers, market makers and our clearing partners - to build good architectural thinking into planning, design, and delivery.
Contribute hands-on to critical work: prototypes, proofs of concept, and production systems that show what scalable, maintainable design looks like.
Set and publish the performance and reliability targets for the domain, and lead the load and resilience testing that proves we can meet them before peak events.
Guide teams in using proven distributed system patterns, so fault tolerance, observability and operational readiness are built in from day one.
Own the operational health of the exchange, using automation and AI tooling to catch problems early and shift from reacting to incidents toward preventing them.
Bring architectural expertise to incidents, help drive resolution, and turn what you learn into fixes that stop the same class of failure recurring.
Keep an eye on cost, and find and pursue efficiency opportunities as the platform grows.
Give thoughtful design reviews and technical guidance that raise the bar across the team, using automated checks for the mechanical issues so review conversations focus on trade-offs.
Mentor engineers - on architecture, and on how to work effectively with AI tools in their own day-to-day.
Look for where AI can genuinely create leverage in the domain, treat it as a real option alongside traditional approaches, and measure adoption by results: faster delivery, fewer defects, lower cost, new capability.
Set sensible guardrails for AI-assisted work - quality checks, boundary validation, and standards that keep things coherent without slowing teams down.
Work with engineering leaders to keep architectural decisions aligned with business priorities and long-term platform strategy.