Engineer IV, Data Engineering

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 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
   

Preferred Skills:

    •    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