Data Engineering Lead: Architect & Scale Pipelines

Nexcess; United States; Hybrid; USD 160,000 - 210,000; Full time

Job summary

Liquid Web Inc. in the United States seeks a Lead, Data Engineer to guide the Data Engineering team and provide technical direction for data infrastructure, pipelines, and systems that support reporting, analytics, and data products.

You will stay hands‑on while mentoring the team and partnering with Finance, Data & Analytics, and other leaders to translate business needs into practical data solutions. The role emphasizes architecture, reliability, and scalability across ETL/ELT, data

Qualifications

• 7+ years of data engineering experience with 2+ years in leadership or technical direction.
• Bachelor’s degree in Computer Science or related field or equivalent.
• Strong SQL, Python/Java/Scala; data pipelines, ETL/ELT, warehouses.
• Experience with cloud data platforms and orchestration tools.
• Hands-on leader with ability to translate business needs into data solutions.

Responsibilities

• Lead, mentor, and develop the Data Engineering team with clear expectations.
• Design, build, and maintain scalable data pipelines and warehouses.
• Provide architectural guidance, ensure reliability, and production readiness.
• Partner with Finance and other stakeholders to translate data needs into solutions.

Skills

Advanced SQL

Python

Java

Scala

Data architecture

Cloud data platforms

Airflow

dbt

Kafka

Spark

CI/CD

Education

Bachelor’s degree in Computer Science or related field

Tools

Airflow

dbt

Kafka

Spark

CI/CD

Cloud platforms (AWS/Azure/GCP)

Job description

Liquid Web Inc. in the United States seeks a Lead, Data Engineer to guide the Data Engineering team and provide technical direction for data infrastructure, pipelines, and systems that support reporting, analytics, and data products.

You will stay hands‑on while mentoring the team and partnering with Finance, Data & Analytics, and other leaders to translate business needs into practical data solutions. The role emphasizes architecture, reliability, and scalability across ETL/ELT, data