Staff Software Engineer

Positively disrupting an industry that has not seen any innovation in over 50 years, Tekion has challenged the paradigm with the first and fastest cloud-native automotive platform that includes the revolutionary Automotive Retail Cloud (ARC) for retailers, Automotive Enterprise Cloud (AEC) for manufacturers and other large automotive enterprises and Automotive Partner Cloud (APC) for technology and industry partners. Tekion connects the entire spectrum of the automotive retail ecosystem through one seamless platform. The transformative platform uses cutting-edge technology, big data, machine learning, and AI to seamlessly bring together OEMs, retailers/dealers and consumers. With its highly configurable integration and greater customer engagement capabilities, Tekion is enabling the best automotive retail experiences ever. Tekion employs close to 3,000 people across North America, Asia and Europe.

We are seeking a Staff Backend Engineer to design and build scalable, AI-native backend services that power customer intelligence, campaign orchestration, and attribution at automotive retail scale. This role is the critical technical anchor — defining hexagonal architecture patterns, owning service boundaries, driving AI integration patterns, and setting the engineering bar for the entire team.

 • Architect Java (Spring Boot 3.x) microservices using hexagonal architecture — defining clean domain cores, port interfaces for persistence (MongoDB, PostgreSQL, Cosmos DB), and adapter implementations for Kafka, Elasticsearch, Redis/Aerospike, and external APIs.

• Own core domain models and service boundaries end-to-end: from initial hexagonal design through production operation, Kafka event contracts, and iterative enhancement.

• Build scalable, secure REST APIs and robust Kafka-based event-driven pipelines that consume and produce events across multiple source systems. 

• Design MongoDB document schemas and PostgreSQL relational schemas for high-throughput, multi-tenant workloads — with deep attention to indexing strategy, TTL, and tenant isolation patterns.

• Design Cosmos DB data models for high-throughput event and state storage — partition key strategy, consistency levels, and TTL configuration. 

• Design and implement durable campaign execution workflows using Temporal. 

• Establish and own AI-native integration patterns: MCP tool registration, scoring model serving contracts, LLM gateway integration, and agentic workflow design.

• Identify and resolve performance bottlenecks across services, Kafka pipelines, data layers, and Elasticsearch query performance. 

• Ensure all systems meet reliability, scalability, and observability standards.

• Mentor senior, mid-level, and junior engineers — setting and consistently raising the engineering bar.

• 8+ years of backend engineering experience with a strong track record delivering production-grade Java systems.

• Deep expertise in hexagonal architecture (ports & adapters) — proven ability to design domain cores fully decoupled from infrastructure layers.

• Strong Java 21 and Spring Boot 3.x expertise with event-driven, microservices-first design. 

• Strong experience with Kafka — consumer groups, partition design, offset management, idempotent consumers, and event-driven service integration.

• Proven experience with MongoDB: document schema design, indexing, aggregation pipelines, and multi tenant isolation patterns.

• Strong PostgreSQL experience: complex schema design, query optimization, migration management.

• Experience with Cosmos DB or equivalent globally distributed key-value stores: partition key design, consistency tuning, high-throughput read/write patterns.

• Solid experience with Elasticsearch: index design, search query optimization, relevance tuning.

• Experience with Redis or Aerospike for high-throughput caching and session/state management.

• Strong understanding of distributed systems: service boundaries, data consistency, idempotency, retries, and circuit breaking on AWS (EKS, MSK).

• Experience with Temporal for durable workflow orchestration. 

• Familiarity with MCP tool registration patterns and agentic platform integration.

• Working knowledge of Python for AI/ML pipeline integration (scoring models, feature serving, LLM gateway patterns).

• Prior experience building data platforms, real-time analytics systems, or AI-native SaaS products.