Engineer IV, Data Engineering at Omnicell
United States
<p>Responsibilities:</p><ul><li>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</li><li>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</li><li>Lead implementation and guide junior engineers on design, coding standards, and best practices</li><li>Develop metadata-driven and configuration-driven pipeline patterns that reduce custom code and improve consistency</li><li>Make technical decisions that ensure reliability, performance, maintainability, and scalability. Ensure production readiness with monitoring, lineage, alerting, observability, CI/CD and documentation</li><li>Define and enforce engineering design patterns, coding standards, testing practices, and operational best practices</li><li>Evaluate and incorporate new technologies and Databricks capabilities that improve reliability, performance, or developer productivity</li><li>Validate new technologies with the Data Architect and operationalize them through documentation, examples, and enablement</li><li>Implement automated data quality checks, rule enforcement, and exception handling</li><li>Production support of both an existing and new platform including optimization of jobs, incident tracking and other analysis required for production</li><li>Lead resolution of complex production issues and deliver durable root cause fixes</li><li>Maintain SLAs for reliability, recovery, idempotency, performance, and cost efficiency</li><li>Mentor Level 2–3 engineers through pairing, design guidance, code reviews, and technical coaching</li></ul><p> </p><p>Basic Skills:</p><p><br> • Bachelor’s degree preferred; equivalent experience accepted<br> • 10+ years in data engineering (12+ without a degree)<br> • 4+ years building production-grade batch/streaming pipelines using PySpark, Spark Structured Streaming, Python, and SQL<br> • Proven experience with data governance, schema evolution, data lineage, and secure access patterns<br> • Proven 2 years’ experience with maintaining and sustaining data pipelines<br> </p><p>Preferred Skills:</p><p><br> • 3+ years hands-on with Databricks (Delta Lake, DLT, Unity Catalog, workflow jobs) within the last 6 years<br> • Experience building metadata-driven or configuration-driven pipelines<br> • Experience with data quality frameworks (DQX, Great Expectations, or equivalent)<br> • Experience with observability, metrics and query performance analysis<br> • Strong Spark optimization<br> </p><p>Work Conditions:</p><p><br> • Team collaborative hours between 8am to 4pm EST<br> • Corporate office/lab environment<br> • Ability to travel 10% of the time</p>
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