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# Databricks Data Engineer - Reliability

- **Company:** Scientific Games
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Architectural Patterns, Information Systems, Extract Transform Load (ETL), Data Systems, Python (Programming Language), Operational Databases, Performance Tuning, SQL Databases, Data Logging, Data Processing, Apache Spark, Data Lakes, Pyspark, Information Technology, Databricks
- **Published:** September 4, 2026
- **Apply:** https://www.dice.com/job-detail/30fe4931-1723-47e0-8676-857394f14c4e

## About the Role

Education Bachelor's degree in Computer Science, Engineering, Information Systems, or related discipline required.

Years of Related Experience 6+ years of related experience, including 3+ years of hands-on Databricks experience developing and supporting production data solutions.

Physical Requirements The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. While performing the duties of this job, the employee is regularly required to sit, stand, walk, bend, use hands, operate a computer, and have specific vision abilities to include close and distance vision, and ability to adjust focus working with computer and business equipment.

## Description

Senior Databricks Data Engineer - Reliability: Designs, develops, and optimizes production Data Products on the Databricks Lakehouse Platform running on AWS, with reliability, observability, performance, scalability, and cost efficiency engineered directly into the solutions built. Engineers monitoring, logging, metrics, dashboards, and proactive alerting to improve diagnostics and production visibility. Identifies recurring reliability issues, performs root cause analysis for complex production issues, and automates recovery and operational activities where practical. Optimizes Spark workloads, Delta Lake, Databricks compute, SQL Warehouses, and storage, diagnosing performance bottlenecks and engineering sustainable improvements while continuously optimizing Databricks and AWS cost efficiency. Develops reusable observability and production-readiness patterns and recovery procedures, supporting appropriate resilience and business continuity requirements. Codes production-grade ETL/ELT and
data-processing pipelines using Python/PySpark, SQL, Spark, and Delta Lake, following approved data models and Medallion Architecture patterns. Leads technical reviews and mentors engineers on Databricks development, observability, troubleshooting, and performance optimization. Note: May be internal or external-facing, working in conjunction with Platform Engineering, Governance Engineering, and enterprise architecture functions. May include enterprise-wide, AI-enabled data solutions.


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