Sales - Software Engineer (Data and Ai Enablement), Singapore Hub

5 days ago

Singapore Apple Full time

**Description**

You’ll design and build core components of our internal data platform, spanning ingestion pipelines, semantic layers, and metadata systems. You’ll help bridge open source technologies (e.g., Kafka, Spark, Iceberg) with our internal ecosystem-shaping how teams discover, use, and trust data for analytics and AI workloads. You’ll collaborate with other engineers, product managers, program managers, and end users to understand data needs and evolve platform capabilities that improve scale, quality, and usability. This is a hands-on engineering role focused on systems thinking, technical craftsmanship, and delivering tools that unlock real business value. Key Responsibilities - Build scalable, cloud-native data systems that support data exploration, reporting, and production ML/AI use cases - Integrate open-source components with internal tools and APIs to streamline platform usability - Develop and maintain data ingestion pipelines, metadata services, and performance-optimized storage layers - Ensure the platform supports AI-readiness, including high-quality, discoverable, and semantically rich data - Collaborate with internal customers to understand workflows and shape new platform features - Partner with engineers, EPMs, and US-based teams to ensure alignment, reusability, and shared standards - Support production systems through monitoring, debugging, and operational improvements

**Minimum Qualifications**
- Proficiency in Python, Scala, or Java, with experience developing scalable and maintainable systems
- Strong SQL skills and experience with cloud data warehouses (e.g., Snowflake, BigQuery)
- Experience with modern data infrastructure tools (e.g., Spark, Kafka, Airflow, Iceberg)
- Understanding of BI and analytics needs, and experience building for internal business use cases

**Preferred Qualifications**
- Familiarity with AI/ML pipeline enablement (e.g., feature engineering, real-time pipelines, metadata)
- Experience with Kubernetes, distributed compute frameworks, or containerized environments
- Hands-on experience integrating with business intelligence or visualization tools (e.g., Streamlit, Tableau, Looker)
- Exposure to anomaly detection, forecasting, or GenAI use cases and the data requirements they demandExperience working in global teams or serving as a technical contributor in a regional hub

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