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# Senior Data Engineering Support Engineer

- **Company:** NTT DATA Corporation
- **Location:** Addison, TX, United States (Remote available)
- **Experience:** Expert
- **Salary:** $92,160 - $138,240
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Automation of Tests, Microsoft Azure, Big Data, Cloud Computing, Cluster Analysis, Code Review, Continuous Integration, Information Engineering, Data Security, Serialization, Distributed Data Store, Distributed Systems, Ganglia (Software), Python (Programming Language), Operational Databases, Performance Tuning, Memory Leaks, Cloud-native Network Functions (CNF), Autoscaling, System Availability, Apache Spark, Mttr, Data Lakes, Pyspark, Git Flow, Data Lineage, Databricks
- **Published:** October 30, 2026
- **Apply:** https://careers-inc.nttdata.com/talentcommunity/apply/1444882700/?locale=en_US

## About the Role

* 7+ years of total experience in Data Engineering.
 * 3+ years dedicated to supporting or optimizing large-scale Databricks and PySpark systems in production.

Preferred Technical skills

 * Advanced Technical Mastery:
 + Advanced PySpark and Python engineering skills, with an explicit understanding of Spark architecture (lazy evaluation, DAG execution, execution memory vs. storage memory).
 + High proficiency in advanced SQL for complex analytical troubleshooting.
 + Expert-level understanding of Delta Lake internals (ACID transactions, Time Travel, Liquid Clustering, Optimize/Z-Order commands).
 * Troubleshooting Excellence: Proven track record of diagnosing and resolving distributed data pipeline issues at scale, including thread dumps, memory leaks, and serialization errors.
 * Cloud & CI/CD Platform Experience:
 + Strong architectural experience deploying and managing resources within major cloud networks (AWS, Azure, or GCP).
 + Robust understanding of Git-based workflows, automated testing, and secure hotfix deployment via CI/CD pipelines.

 * Active Databricks Certifications (e.g., Databricks Certified Data Engineer Professional).
 * Deep experience managing production environments utilizing Delta Live Tables (DLT) and advanced Unity Catalog rule engines.

## Description

We are seeking a Senior Data Engineering Support Engineer with expert-level proficiency in Databricks and PySpark. In this role, you will be the primary technical anchor for our enterprise big data operations, driving high availability, performance efficiency, and cost optimization across all production data pipelines. Beyond resolving complex production incidents, you will lead root-cause analysis (RCA), design automated self-healing scripts, and mentor junior support staff to ensure the overall stability of our Delta Lakehouse environment., * Critical Incident & Escalation Management: Act as the highest tier of internal escalation for complex, high-priority production failures in massive-scale batch and streaming PySpark jobs.

 * Deep-Dive Performance Tuning: Drive the resolution of complex distributed computing bottlenecks, including severe data skew, Out-Of-Memory (OOM) errors, improper partition pruning, and costly shuffle operations using Spark UI, logs, and Ganglia metrics.
 * Cost & Cluster Optimization: Analyze Databricks cluster utilization and run strategies to reduce cloud compute spend (e.g., configuring auto-scaling, spot instances, and choosing optimal driver/executor node types).
 * Governance & Reliability Frameworks: Oversee and troubleshoot data access control, schema evolution, and data lineage tracking within Databricks Unity Catalog.
 * Automation & SRE Mindset: Build robust, automated frameworks and scripts in Python/PySpark to automate data reconciliation, system health checks, and self-healing pipelines.
 * Strategic Playbooks & Documentation: Author comprehensive, technical runbooks and post-mortem RCA documents. Establish SOPs (Standard Operating Procedures) to reduce future incident resolution time (MTTR).
 * Mentorship & Leadership: Provide technical guidance to junior support engineers, conduct technical code reviews for hotfixes, and closely collaborate with core development teams to bridge the gap between engineering and operations.