Data Engineer

A highly successful, profitable health-intelligence technology company that applies advanced patterns from elite data intelligence methodologies to the medical domain.

By merging sophisticated predictive AI models with high-scale data extraction frameworks, the platform synthesizes unstructured clinical telemetry into actionable insights for healthcare networks globally, operating as a core engine within the modern clinical intelligence landscape.

The company operates out of a major tech hub in the central district (optimally situated near primary mass transit nodes) and offers an exceptionally flexible hybrid model with up to three days of remote work.

Key Responsibilities

• Architecting, building, and maintaining high-throughput, low-latency ETL/ELT pipelines to ingest, normalize, and process dense multi-source operational datasets.
• Designing scalable database schemas, optimizing complex analytical query layers, and managing distributed data storage structures.
• Constructing and scaling internal platform components and specialized API endpoints within containerized, cloud-native frameworks.
• Structuring and provisioning serverless orchestration workflows, continuous testing gates, and automated deployment pipelines.
• Profiling data infrastructure branches to eliminate operational data lag, maximize parsing throughput, and ensure end-to-end data integrity.
• Tech Stack & Environment Python, SQL, ETL/ELT Architectures, AWS, Serverless Compute, Kubernetes, CI/CD, and Advanced Data Frameworks (Kafka / Snowflake / Spark).

Requirements

• 5 years of professional engineering experience dedicated exclusively to Data Engineering or high-scale data systems architecture (Mandatory).
• Deep technical command and production-grade software development proficiency utilizing Python (Mandatory).
• Comprehensive, hands-on experience building, scaling, and deploying data infrastructures within public Cloud environments, preferably AWS (Mandatory).
• Solid engineering expertise designing, optimizing, and monitoring enterprise-scale ETL pipelines (Mandatory).
• Extensive production-grade experience utilizing at least one of the following distributed data or streaming frameworks: Kafka, Snowflake, or Spark (Mandatory).
• Proven track record of delivering scalable data architectures specifically within SaaS product environments (Mandatory).
• Exceptional systems-design capabilities, with a deep understanding of cloud design patterns, data lake architectures, and high-concurrency storage systems.