[Hiring] Expert Software Engineer II @Alegeus

Role Description

We are looking for an Expert Software Engineer with a strong product mindset and test-driven development (TDD) background to join our consumer-directed healthcare platform engineering team. As a backend application and data-platform developer, you will build Java and Spring Boot microservices and enterprise-scale data capabilities that support reliable data acquisition, ingestion, storage, processing, and consumption.

This role goes beyond writing code to a requirement specification. We need an engineer who understands the business value behind every feature, collaborates closely with product and stakeholders, and takes end-to-end ownership of outcomes.

Responsibilities

• Application and microservice development
• Collaborate on domain decomposition, domain modeling, and domain-driven design (DDD) for new microservices and data domains.
• Design, implement, test, and deploy scalable Java and Spring Boot microservices using RESTful APIs, asynchronous processing, and event-driven patterns.
• Build resilient Kafka-based producers and consumers, including topic design, partitioning, consumer-group management, schema evolution, replay, ordering, and dead-letter handling.
• Use SQL databases and data-access frameworks to create efficient schemas, queries, transactions, indexing strategies, and data lifecycle practices.
• Interface down the stack with legacy APIs and systems where subdomain remapping, data reconciliation, or contract modernization may be required.
• Champion test-driven development and write automated unit, integration, contract, performance, and regression tests.
• Leverage AI-assisted tools for code generation, test-case generation, documentation, and developer productivity.
• Run code reviews, static analysis, and refactoring sessions to improve code quality and maintainability.
• Deploy continuously from local development through staging using CI/CD, with frequent production releases.
• Actively instrument applications and data flows for effective logging, metrics, tracing, alerting, and operational observability.
• Enterprise-scale data platform engineering
• Design and evolve data-platform capabilities across the full lifecycle: data acquisition, ingestion, validation, transformation, storage, serving, and consumption.
• Develop reliable ingestion patterns for APIs, events, files, and operational systems, with idempotency, deduplication, replayability, back-pressure, and failure recovery.
• Define data contracts, canonical models, metadata, lineage, quality rules, retention policies, and governance practices in partnership with platform and domain teams.
• Build fit-for-purpose storage and serving patterns using SQL and complementary enterprise data stores as appropriate for transactional, analytical, and operational workloads.
• Enable secure and performant data consumption through APIs, events, query services, reporting interfaces, and downstream data products.
• Design for high availability, scalability, disaster recovery, data security, privacy, and regulatory needs across enterprise data flows.
• Monitor data freshness, completeness, accuracy, throughput, latency, and cost; use these measures to drive continuous improvement.
• Product mindset and collaboration
• Actively engage with product managers, product owners, and business stakeholders to understand why features are being built, propose alternative approaches, and weigh engineering and product trade-offs.
• Think beyond the task at hand to deeply understand the purpose and business goals.
• Work cross-functionally with quality assurance, infrastructure, data, security, design, and operations to deliver coherent solutions.
• Ownership and accountability
• Take end-to-end ownership for designing, building, and delivering features and data capabilities.
• Provide ongoing maintenance, support, and enhancements in existing systems, services, and data platforms.
• Communicate progress, raise risks early, and drive resolutions.
• Continuous improvement
• Recommend and implement improvements to engineering processes, architecture, data quality, reliability, performance, and cost efficiency.
• Stay current on best practices in Java, Spring Boot, Kafka, SQL, distributed systems, cloud engineering, data platforms, and AI in software engineering.
• Mentor peers on test-driven development, DDD, event-driven architecture, data-platform design, and software craftsmanship.

Qualifications

• 7+ years of professional software engineering experience at a software product company.
• Strong test-driven development (TDD) background with demonstrated experience writing unit and integration tests.
• Strong proficiency in Java and Spring Boot.
• Hands-on experience with Apache Kafka and event-driven architectures.
• Strong SQL experience with relational database design.
• Experience designing or contributing to enterprise-scale data platforms.
• Experience deploying services to Azure or other cloud platforms.
• Excellent communication and presentation skills.

Preferred qualifications

• Bachelor’s or master’s degree in computer science, engineering, or a related field.
• Experience with cloud-native data services, data lakes or warehouses, stream processing.
• Experience developing applications using micro-frontend architecture.
• Familiarity with healthcare or payment-processing domains.
• Experience using generative AI for code generation, testing, documentation, and data engineering productivity.

Benefits

• A flexible work environment
• Competitive salaries, paid vacation, and holidays
• Robust professional development programs
• Comprehensive health, wellness, and financial packages