Lead, Test Data Engineering & Data Quality

Live Nation Entertainment Inc; Florida; On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Medical benefits

Free concert tickets

Paid time off

401(k) plan

Tuition reimbursement

Job summary

Live Nation Entertainment is seeking a Test Data Engineering Manager to lead a globally distributed team focused on automated data testing, synthetic data generation, and regression frameworks within a Databricks-based platform.

You will drive data quality initiatives across North America, Europe, and Asia, partnering with engineering, product, and analytics to deliver reliable data solutions and observability. This role emphasizes TDD practices and scalable testing across pipelines.

Qualifications

• 5+ years in Data Engineering, Test Data Engineering, or related fields.
• ~2+ years in a technical leadership or management role.
• Strong experience with Databricks, Spark, distributed data systems, Python and SQL.
• Experience building automated validation or data quality frameworks.
• Hands-on with data observability or validation tools; AI orchestration a plus.

Responsibilities

• Design and implement reusable data quality frameworks in Databricks pipelines.
• Define data quality standards and the Definition of Done across pipelines.
• Build scalable synthetic data generation and masking solutions.
• Create automated regression testing for batch and streaming pipelines.

Skills

Data engineering

Test data engineering

Python

SQL

Databricks

Spark

Data validation

Agile

Tools

Databricks

Spark

JIRA

Job description

Live Nation Entertainment is seeking a Test Data Engineering Manager to lead a globally distributed team focused on automated data testing, synthetic data generation, and regression frameworks within a Databricks-based platform.

You will drive data quality initiatives across North America, Europe, and Asia, partnering with engineering, product, and analytics to deliver reliable data solutions and observability. This role emphasizes TDD practices and scalable testing across pipelines.