Senior Data Software Engineer

We are looking for a highly skilled Senior Data Software Engineer to join our team and take a key role in building high-performance, reliable, and scalable data systems. In this position, you will own real-time data pipelines, tracking infrastructure, feature store foundations, and the systems that power analytics and production ML.

This is a critical role for a strong engineer who thrives in complex distributed environments and enjoys building robust data platforms that serve real business impact.

As a Senior Data/Software Engineer, you will architect and develop real-time, low-latency data services and high-throughput ingestion pipelines. You will collaborate closely with analytics, ML, and product teams to ensure data availability, high quality, and consistency across online and offline environments. You will design scalable systems, enforce best engineering practices, and drive operational excellence in production.

• Build and maintain high-performance, real-time data pipelines and services.
• Develop and operate distributed systems that support large-scale data ingestion and processing.
• Design schemas, ingestion flows, and data quality frameworks to ensure reliable, consistent data.
• Partner with ML and analytics teams to streamline feature availability and cross-environment consistency.
• Contribute to architecture discussions and system design across the data platform.
• Implement monitoring, alerting, and observability for data pipelines in production.
• Ensure strong engineering standards: clean code, testing, reviews, documentation.
• Drive continuous improvements in performance, reliability, and scalability.

• Strong software engineering fundamentals (clean code, testing, system design).
• Proven experience building real-time, low-latency data services.
• Hands-on experience operating systems in production at scale (high throughput, distributed systems).
• Expertise with the modern data stack such as:
• Kafka / Kinesis
• Spark / Flink / Beam
• Snowflake / BigQuery / Redshift
• Ability to design scalable schemas, ingestion pipelines, and data quality frameworks.
• Experience collaborating with ML teams on feature access, consistency, and data lifecycle.

• Experience building or supporting ML systems (feature serving, online/offline consistency).
• Production experience with model inference pipelines.
• Strong DevOps background: Docker, Kubernetes, CI/CD.
• Experience with cloud-native architectures (AWS or GCP).
• Experience implementing data observability, lineage, and monitoring frameworks.