Lead Software Developer

Accountabilities:: Lead the design and evolution of enterprise data-platform architecture, establishing standards and best practices for data storage, integration, analytics, and governance. Drive the adoption and implementation of Snowflake-based solutions for reporting, analytics, AI-enabled data products, and enterprise data-warehouse initiatives. Own database architecture, performance optimization, operational reliability, and cost management across Snowflake, PostgreSQL, and SQL Server environments. Design and maintain scalable data-ingestion, transformation, and integration solutions capable of supporting high-volume application data. Develop analytics and self-service reporting capabilities, including semantic models, BI integrations, and intelligent data experiences. Contribute to Snowflake Cortex-powered solutions, including AI-assisted analytics and AI-enabled data products where appropriate. Partner with backend engineering teams to design and evolve scalable Java services, REST APIs, and distributed systems supporting business-critical applications. Develop cloud-native solutions using AWS services and modern DevOps and infrastructure-as-code practices. Build Python-based automation, orchestration, data-processing, and platform-tooling solutions. Collaborate with architects, product owners, and business stakeholders to define scalable, maintainable, and long-term technical solutions. Establish engineering standards, promote platform consistency, and drive operational excellence across multiple teams. Mentor engineers, share technical expertise, and influence engineering practices beyond the immediate team. Requirements: Bachelor's degree in Computer Science, Software Engineering, or a related technical discipline. 10+ years of professional software engineering experience building enterprise-scale applications, platforms, or data systems. Demonstrated experience leading complex technical initiatives with a high degree of autonomy. Strong relational database expertise, particularly with SQL Server or PostgreSQL, including query optimization and performance tuning for transactional systems. Proven experience designing, implementing, and operating Snowflake-based data platforms in production environments. Strong understanding of data engineering, data warehousing, dimensional modeling, and OLAP concepts. Experience with modern business intelligence and analytics platforms such as Sigma, Power BI, Tableau, or comparable technologies. Experience with Snowflake Cortex, semantic views, AI/ML capabilities, agents, AI-assisted analytics, or AI-powered data products is highly desirable. Strong Java development experience with technologies such as Spring Boot, Hibernate/JPA, REST APIs, and microservice architectures. Strong AWS experience, including services such as EC2, S3, Lambda, Step Functions, and infrastructure-as-code solutions. Strong Python development skills for automation, orchestration, data engineering, and platform tooling. Understanding of distributed systems, event-driven architectures, and messaging technologies such as Kafka. Practical experience using AI tools to improve software development productivity and workflows. Strong communication, mentoring, stakeholder-management, and technical leadership abilities. Proven ability to influence technical direction across teams and drive adoption of engineering standards and best practices. Ability to work effectively in a collaborative environment while operating independently on complex technical problems. Benefits: Competitive annual compensation, with the final salary dependent on location, job-related knowledge, skills, and experience. Hybrid working arrangement, subject to the applicable office-location requirements. Opportunity to work on enterprise-scale financial technology, data, analytics, and cloud initiatives. Exposure to modern technologies including Snowflake, Snowflake Cortex, AWS, Java, Python, distributed systems, and AI-enabled data products. High level of technical ownership and influence across multiple engineering teams. Opportunities for mentoring, technical leadership, and professional development. Collaborative environment involving engineering, architecture, product, data, and business stakeholders. Opportunity to contribute to the evolution of scalable, governed, and auditable data and analytics platforms.