Data Reliability Engineer

Vytalize Health

Data Reliability Engineer ensuring operational health of healthcare data pipelines at Vytalize Health. Focused on data quality, compliance, and collaboration across teams.

Posted 7/15/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and maintaining reliable data pipelines, ensuring data quality and observability through established standards and frameworks. Proficient in Python and SQL, with a strong understanding of modern data architectures and cloud-based data platforms.

Highest-signal resume keywords

Data Pipeline ReliabilityService Level Indicators (SLIs)Data Quality TestingMachine Learning-Based MonitoringCloud-Based Data Platforms

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonSQLData Quality TestingData Pipeline DesignObservability FrameworksIncident ResponseData Validation FrameworksData Lakehouse PatternsDistributed Data SystemsData Models

Soft Skills

CommunicationProblem-SolvingLeadership

Tools & Technologies

AWSDatabricksAI-Assisted Observability Tools

Industry Keywords

Service Level Objectives (SLOs)Service Level Agreements (SLAs)Data IngestionData ConsumptionAnomaly Detection

Tech Stack

Tools & technologies

AWSCloudPythonSQL

About the role

Key responsibilities & impact

Own and continuously improve the reliability of data pipelines across ingestion, transformation, and delivery layers, ensuring data is accurate, complete, and delivered on schedule.
Establish and maintain data reliability standards, including Service Level Indicators (SLIs), Service Level Objectives (SLOs), and Service Level Agreements (SLAs) for both upstream ingestion and downstream data delivery.
Design, implement, and maintain comprehensive monitoring, logging, and observability frameworks for data pipelines, datasets, and data services with clear visibility into freshness, volume, schema changes, and data quality.
Design and implement data quality testing and validation frameworks — establishing test cases, golden datasets, and regression tests to detect quality issues early.
Lead incident response for data reliability issues, including detection, triage, communication, root cause analysis, and post-incident remediation with documented corrective actions.
Drive improvements in pipeline resiliency through retry strategies, backfills, idempotency, schema enforcement, and safe deployment practices.

Requirements

What you’ll need

Bachelor's degree in Computer Science, Engineering, Information Systems, or related field, or equivalent professional experience.
5+ years of experience working with data platforms, data pipelines, or distributed data systems in production environments.
Demonstrated experience improving reliability, observability, or operational quality of data systems with measurable SLI/SLO/SLA improvements.
Hands-on experience supporting both data ingestion pipelines and downstream data consumption or delivery patterns.
1+ years of hands-on experience with machine learning-based monitoring, anomaly detection, or AI-assisted observability tools.
Demonstrated experience with data quality testing, validation frameworks, and quality metrics definition.
Proficiency in Python and SQL, with experience building or supporting production-grade data pipelines.
Strong understanding of modern data architectures, including data lakehouse patterns and multi-layer (bronze/silver/gold) data models.
Experience with cloud-based data platforms (AWS, Databricks, or similar).

Benefits

Comp & perks

Health insurance
401(k) matching
Flexible work arrangements
Professional development opportunities