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.

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.