• Design, develop, and maintain fullstack software solutions leveraging advanced data analytics and artificial intelligence to solve complex client challenges with precision and innovation. • Collaborate with cross-functional teams — including product, design, and operations — to translate business needs into elegant, scalable, and high-performing systems. • Champion engineering excellence through clean architecture, performance optimization, and robust coding standards, ensuring reliability, maintainability, and long-term scalability. • Participate in design reviews, testing, and continuous integration to deliver top-quality, production-grade software that meets or exceeds expectations. • Stay ahead of industry trends by researching and adopting emerging technologies, driving continuous innovation and maintaining SnowHeap’s competitive edge in AI-driven software development.

Role Description The Cint Technology team is looking for a Senior Software Engineer to join the methodology team, which owns the core product customers interact with. This role is for a backend engineer who enjoys working on high-volume, performance-sensitive systems and complex technical challenges. The team works across two main areas: - Recruitment, which consumes third-party survey responses and evaluates them with backend algorithms. - Lift, which is being migrated from HTTPS/API REST toward event-driven integrations. The broader platform is also moving from a legacy monolith toward a domain-based architecture. You will work on systems that are write-heavy, asynchronous, and critical to the customer experience. The product is Tier 1, so reliability, scalability, and performance are key. Qualifications - 10+ years of backend software engineering experience, with strong technical depth. - We’re technology agnostic, so you’re more than welcome to switch your main language (Python, Scala, C/C++, Haskell, Elixir, Go, Ruby) to Java (core language). - Solid understanding of algorithms, data structures, and Big O notation. - Experience with Python and/or PySpark. - Experience building or maintaining complex, high-volume systems. - Familiarity with heavy data processing tools and technologies such as Spark, Apache ecosystem tools, Delta Lake, Parquet, Kafka, and Avro. - Experience with Docker, SQL, and NoSQL databases. - Experience with REST APIs and event-driven integrations. - Comfortable working in performance-oriented, production-critical environments with high-throughput & low latency systems. - Strong problem-solving skills and a proactive, collaborative working style. - Ability to communicate well with engineers and data science partners. - Background in computer science, computer engineering, or a related field is preferred. Requirements - Experience with AWS. - Experience with Pandas. - Experience with Spring framework and microservices. - Experience working in large-scale data or analytics environments. Benefits - Collaboration is our superpower. - Innovation is in our blood. - We do what we say. - Excellence comes as standard. - We are caring.

• Develop and maintain the Manufacturing Data Platform, orchestrating data exchange between systems and manufacturing operations. • Ensure seamless data flow across all production systems and maintain 24/7 operational reliability. • Design and implement robust data exchange pipelines between manufacturing systems. • Develop and maintain core manufacturing database features and integrations. • Develop and maintain system monitoring and observability solutions. • Ensure ERP synchronization and accurate data flow across manufacturing systems. • Support integration with data analytics platforms and historical data migration. • Optimize system performance and reliability, ensuring operational uptime. • Provide technical consultation on data-related topics and system integrations.

• Core Platform Engineering: Build and scale highly resilient, adaptable fintech platforms and component services utilizing core Java. • Extreme Programming Collaboration: Engage in full-time pair programming to foster knowledge sharing, maintain zero technical silos, and optimize code quality. • Continuous Refactoring: Constantly analyze and refactor production code to elevate design frameworks, minimize technical debt, and preserve system agility. • Comprehensive Test Architecture: Author robust automated test suites, participating in a sophisticated continuous integration ecosystem where thousands of tests rapidly validate every single commit. • Process Automation: Identify, script, and automate repetitive tasks to optimize operational efficiency across the development lifecycle. • Trunk-Based Delivery: Operate within a mature trunk-based development and continuous integration setup, treating each code modification as an immediately releasable asset. • Cross-System Rotation: Shift periodically across teams to maintain comprehensive visibility into the entire codebase, driving an shared ownership mindset. • Performance Optimization: Account for throughput, high availability, and ultra-low latency constraints when delivering core features.