Engineer IV, Data Engineering

Omnicell • Full-time • Remote (Pittsburgh, PA, US) • 1w ago

Responsibilities

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

Basic Skills

Bachelor’s degree preferred; equivalent experience accepted
10+ years in data engineering (12+ without a degree)
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

Preferred Skills

3+ years hands-on with Databricks (Delta Lake, DLT, Unity Catalog, workflow jobs) within the last 6 years
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
Corporate office/lab environment
Ability to travel 10% of the time

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