Data Reliability Engineer
Vytalize Health · Remote · Remote ·
COMPENSATION
€•• – ••k
Listed on the employer's job board (Ashby)
- Fully remote
- 5+ years experience
- Python + SQL
- Healthcare data focus
- AI observability tools
This position sits at the intersection of Data Engineering and Data Services, ensuring data is accurate, timely, and dependable for internal and external consumers. You will partner with cross-functional teams to define reliability standards for mission-critical healthcare data products.
The role emphasizes automation, failure prevention, and rapid recovery while maintaining strict compliance with security and privacy regulations. You will establish clear reliability targets and measure success through data quality and delivery timeliness.
What you'll do
- Own and improve reliability of data pipelines across ingestion, transformation, and delivery layers.
- Design and maintain monitoring frameworks for data freshness, volume, schema changes, and quality.
- Establish data quality metrics and track accuracy, completeness, and consistency across systems.
- Leverage AI and machine learning tools to detect anomalies and reliability risks proactively.
- Ensure practices align with healthcare security, privacy, and regulatory compliance requirements.
- Support capacity planning by analyzing pipeline performance and identifying architectural improvements.
What you'll need
- Bachelor's degree in Computer Science, Engineering, or related field, or equivalent experience.
- 5+ years working with data platforms, pipelines, or distributed systems in production.
- Proven experience improving reliability and observability with measurable SLI/SLO improvements.
- 1+ year of hands-on experience with ML-based monitoring or AI-assisted observability tools.
- Proficiency in Python and SQL for building or supporting production-grade data pipelines.
- Strong understanding of modern data architectures, including lakehouse patterns and multi-layer models.
- Experience with cloud-based data platforms such as AWS or Databricks.
Nice to have
- Experience with ML-based anomaly detection frameworks like Datadog Anomaly Detection.
- Familiarity with healthcare data standards such as FHIR, HL7, and claims data formats.
- Experience operating observability and incident management platforms like PagerDuty.
- Background in startup or high-growth environments with exposure to scaling data systems.
- Familiarity with clinical data normalization frameworks like Tuva.
Stack
- Python
- SQL
- AWS
- Databricks
- Airflow
- Datadog
- New Relic
- PagerDuty