Data Engineering Manager

Data Engineering Manager Addison, TX Job Details 17 hours ago Qualifications Data model design SSO Cloud identity and access management (IAM) Data modeling Cloud infrastructure Data transformation pipeline development SQL databases Automation Kubernetes Data migration Big data Infrastructure as Code (IaC) Spark Employee retention Apache Hive Java 8 years SQL Data quality management Team development RBAC Data Architecture Design (Architecture design skills) Spark implementation Team management Distributed systems Scala Terraform Agile software development Linux Kafka Data validation Distributed computing Senior level Leadership Jenkins GitLab Communication skills Python Stakeholder relationship building Identity & access management Stakeholder management

Full Job Description Description:

The Data Engineering Manager will lead distributed teams responsible for designing, modernizing, and operating large-scale data pipelines on the NexusOne platform — an open-source-based data and AI control plane for regulated enterprise environments. This leader combines hands-on technical depth with strategic delivery management, ensuring that complex data workloads are refactored, migrated, and optimized using modern, composable frameworks. The role requires strong architectural intuition, proven leadership, and fluency in modern data-engineering technologies.

Core Responsibilities Must-Have Core Skills Distributed Data Systems:

Deep expertise in Apache Spark (core, SQL, streaming) and Airflow orchestration.

Open-Source Data Stack:

Hands-on with Iceberg , Trino/Presto , Hive , NiFi , and Kafka .

Data Modeling & Transformation:

Strong command of

SQL , DBT

, and schema-on-read design principles.

Programming & Scripting:

Proficiency in Python , Scala , and Java for data pipeline development.

Infrastructure & Automation:

Practical experience with Kubernetes , Terraform , Helm , GitLab CI/Jenkins , and Linux-based environments.

Governance & Security:

Familiarity with Ranger , DataHub , RBAC, and enterprise IAM/SSO integration.

Data Quality & Testing:

Working knowledge of Great Expectations or similar validation frameworks.

Delivery Leadership:

Proven ability to manage distributed engineering teams in Agile/DevOps settings. Ideal Background 8-12 years of experience in data engineering or platform modernization , including 3+ years leading technical teams. Strong foundation in open-source and hybrid-cloud data ecosystems . Demonstrated success leading migration or modernization programs at enterprise scale. Excellent communication and stakeholder-management skills; capable of bridging executive vision and technical execution. Success Criteria Delivery of high-performing, production-ready data pipelines on the NexusOne platform. Consistent adherence to governance, quality, and performance SLAs . Adoption of standardized frameworks and automation across engineering teams. Measurable improvement in pipeline reliability, cost efficiency, and developer productivity . High engagement, retention, and growth within the data-engineering team.

Requirements:

Must-Have Core Skills Distributed Data Systems:

Deep expertise in Apache Spark (core, SQL, streaming) and Airflow orchestration.

Open-Source Data Stack:

Hands-on with Iceberg, Trino/Presto, Hive, NiFi, and Kafka.

Data Modeling & Transformation:

Strong command of SQL, DBT, and schema-on-read design principles.

Programming & Scripting:

Proficiency in Python, Scala, and Java for data pipeline development.

Infrastructure & Automation:

Practical experience with Kubernetes, Terraform, Helm, GitLab CI/Jenkins, and Linux-based environments.

Governance & Security:

Familiarity with Ranger, DataHub, RBAC, and enterprise IAM/SSO integration.

Data Quality & Testing:

Working knowledge of Great Expectations or similar validation frameworks.

Delivery Leadership:

Proven ability to manage distributed engineering teams in Agile/DevOps settings. Ideal Background 8-12 years of experience in data engineering or platform modernization, including 3+ years leading technical teams. Strong foundation in open-source and hybrid-cloud data ecosystems. Demonstrated success leading migration or modernization programs at enterprise scale. Excellent communication and stakeholder-management skills; capable of bridging executive vision and technical execution. Success Criteria Delivery of high-performing, production-ready data pipelines on the NexusOne platform. Consistent adherence to governance, quality, and performance SLAs. Adoption of standardized frameworks and automation across engineering teams. Measurable improvement in pipeline reliability, cost efficiency, and developer productivity. High engagement, retention, and growth within the data-engineering team.