Senior Software Engineer, Bet Engine

New

JobgetherSports betting

Based in CanadaFull-TimeSenior

Salary$145,000–$193,000 USD annually

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Job Details

Experience: At least 5 years of professional software engineering experience; at least 2 years of experience designing and evolving GraphQL schemas.
Required Skills: GraphQLNode.jsPostgreSQLKubernetesMySQLRuby on RailsApache KafkaFastAPIgRPCDatadog

At least 5 years of professional software engineering experience.
Strong computer science foundation in data structures, algorithms, distributed systems, and software design.
Experience operating production systems at scale, including load testing, tracing, and performance analysis.
Experience with production on-call rotations and end-to-end software release cycles.
Experience with modern web frameworks and API development, such as Phoenix, Ruby on Rails, FastAPI, Laravel, or Node.js.
At least 2 years designing and evolving GraphQL schemas for products serving multiple clients.
Experience building multi-client or white-label platforms, including geo-based routing and client-specific configuration.
Strong relational database knowledge, particularly PostgreSQL and MySQL, including normalization, denormalization, and performance tradeoffs.
Experience with Kubernetes and operating services in containerized environments.
Experience with testing frameworks and background job processing technologies.
Experience with asynchronous event-processing infrastructure such as Apache Kafka, RabbitMQ, AWS SQS/SNS, or Google Cloud Pub/Sub.
Strong written and verbal communication, including technical specifications, runbooks, and cross-team design documentation.

Design, scale, and tune GraphQL APIs, low-latency gRPC services, and SQL queries for wagering and related systems.
Develop solution proposals for complex technical problems and present designs to leadership and partner teams.
Architect and deliver features end-to-end in an Agile/Scrum environment.
Identify production bottlenecks, performance risks, reliability issues, and scalability constraints, then drive improvements.
Establish and refine Datadog monitoring and alerting for owned systems and services.
Investigate and remediate production performance regressions involving CPU, I/O, memory, and latency.
Mentor engineers through technical specifications, pairing, code reviews, and collaborative problem-solving.
Integrate internal, third-party, legacy, and modern systems through data migrations, data translation, and supporting infrastructure.

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