[Hiring] Engineer IV, Data Engineering @Omnicell

Role Description

• Translate business needs and architectural guidance into detailed designs, data contracts, and implementation plans that break down large initiatives into actionable engineering tasks with reliable estimates.
• Create detailed pipeline designs covering schemas, transformations, partitioning, DLT configurations, orchestration, error handling, and observability that align with the platform architecture through close collaboration with the Data Architect.
• Lead implementation and guide junior engineers on design, coding standards, and best practices.
• Develop metadata-driven and configuration-driven pipeline patterns that reduce custom code and improve consistency.
• Make technical decisions that ensure reliability, performance, maintainability, and scalability. Ensure production readiness with monitoring, lineage, alerting, observability, CI/CD, and documentation.
• Define and enforce engineering design patterns, coding standards, testing practices, and operational best practices.
• Evaluate and incorporate new technologies and Databricks capabilities that improve reliability, performance, or developer productivity.
• Validate new technologies with the Data Architect and operationalize them through documentation, examples, and enablement.
• Implement automated data quality checks, rule enforcement, and exception handling.
• Production support of both an existing and new platform including optimization of jobs, incident tracking, and other analysis required for production.
• Lead resolution of complex production issues and deliver durable root cause fixes.
• Maintain SLAs for reliability, recovery, idempotency, performance, and cost efficiency.
• Mentor Level 2–3 engineers through pairing, design guidance, code reviews, and technical coaching.

Qualifications

• Bachelor’s degree preferred; equivalent experience accepted.
• 10+ years of experience in data engineering (12+ without a degree).
• 3+ years hands-on with Databricks (Delta Lake, DLT, Unity Catalog, workflow jobs) within the last 6 years.
• 4+ years building production-grade batch/streaming pipelines using PySpark, Spark Structured Streaming, Python, and SQL.
• Proven experience with data governance, schema evolution, data lineage, and secure access patterns.
• Proven 2 years’ experience with maintaining and sustaining data pipelines.

Requirements

• Experience building metadata-driven or configuration-driven pipelines.
• Experience with data quality frameworks (DQX, Great Expectations, or equivalent).
• Experience with observability, metrics, and query performance analysis.
• Strong Spark optimization.

Work Conditions

• Team collaborative hours between 8am to 4pm EST.
• Remote corporate office/lab environment.
• Ability to travel 10% of the time.