Software Engineer (Data)

Our client provides a platform for intelligent marketing automation used by customers from over 170 countries. The role is part of the client's growing AI and Data organization, focusing on building scalable foundations for next-generation data and ML systems, shaping software architecture, engineering standards, and production-ready platforms powering the AI ecosystem.

• 4+ years of experience in software engineering with strong hands-on backend development and building scalable production systems.
• Strong proficiency in Python and solid software engineering fundamentals, including clean architecture, testing, and maintainable code practices.
• Experience with data-intensive applications, databases, SQL, data modeling, and query optimization.
• Exposure to modern data engineering, cloud, or infrastructure environments, familiar with Terraform, Kubernetes, Docker, or similar technologies.
• Understanding of CI/CD pipelines, Infrastructure as Code, and general engineering best practices.
• Interest in AI/ML ecosystems and willingness to work closely with Data Science and AI teams on productionizing ML solutions.
• Familiarity with cloud platforms and modern data stack technologies such as AWS, Snowflake, Spark, or DBT is a strong plus.
• Ownership mindset and comfort working in evolving, fast-moving environments where systems and processes are still being built.

• Design, develop, and maintain scalable high-throughput data pipelines across complex data ecosystems.
• Build and optimize data models, schemas, and database structures to ensure long-term scalability and performance.
• Implement engineering best practices, including automated testing, CI/CD pipelines, and Infrastructure as Code (Terraform).
• Partner closely with AI and Data Science teams to productionize machine learning solutions and develop scalable MLOps pipelines.
• Engineer and maintain feature stores and data infrastructure supporting AI-driven initiatives.
• Monitor, maintain, and improve the reliability and efficiency of containerized environments using Kubernetes, Docker, and Airflow.
• Ensure platform stability, observability, and operational excellence through proactive system monitoring and health checks.
• Collaborate cross-functionally with engineering and business stakeholders to translate complex technical concepts into actionable insights.
• Contribute to the architectural direction and scalability of the organization’s AI and data platforms.
• Drive the adoption of robust software engineering standards across data and infrastructure projects.