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# Senior Engineering Manager, Data Engineering

- **Company:** Crisis Text Line
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $135,520.0 - $156,790.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Information Engineering, Data Governance, Data Integrity, Data Systems, Python (Programming Language), Machine Learning, DataOps, Azure Machine Learning, SQL Databases, Technical Debt, Infrastructure Automation Frameworks, Machine Learning Operations, Terraform, Data Pipelines, Databricks
- **Published:** September 13, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=85f204e0750a9379

## About the Role

Required

 * 10+ years in data engineering, infrastructure, or ML platforms; 5+ years in people leadership.
 * Proven experience building Databricks + AWS ecosystems.
 * Proficiency in Python, SQL, and Terraform with strong AI Engineering principles.
 * Track record improving observability, automation, and data reliability.
 * Experience with data governance and compliance (HIPAA/SOC 2).
 * Excellent communication and stakeholder partnership across technical and non-technical teams.

Preferred

The following experience would be useful, but we encourage you to apply even if you do not meet every item:

 * Built or scaled an AI Engineering function or major data modernization program.
 * Managed global or multi-region data environments.
 * Experience balancing innovation with operational stability and cost control.
 * Mix of enterprise discipline and startup agility.

Leadership Fit

 * Coaching-oriented, people-first manager who leads with clarity and trust., Reliable High-Speed Internet Required: Must have a stable high-speed internet connection to support seamless remote collaboration, virtual meetings, online job tasks, etc.

## Description

* Oversee the evolution of our Databricks-based lakehouse and ML platform, defining the technical roadmap for data pipelines and AI/ML infrastructure.
 * Drive infrastructure-as-code practices with Terraform and make key architectural decisions that enable scalable, reliable global data systems.

Platform Maturity

 * Ensure reliability, performance, and cost efficiency across Databricks and AWS by implementing observability, monitoring, and SLA frameworks for critical data pipelines.
 * Establish best practices for DataOps, AI Engineering, and data quality while balancing innovation with technical debt and tracking KPIs for platform health, productivity, and business impact.

Cross-functional Collaboration

 * Partner with Product, Analytics, Data Science, and Research teams to deliver scalable, compliant data solutions.
 * Collaborate with Developer Platform, Security, and Compliance teams to align shared infrastructure, governance, and business priorities.

Team Leadership & Development

 * Build, mentor, and scale a high-performing data and ML engineering team focused on innovation and reliability.
 * Foster a culture of technical excellence, collaboration, and continuous learning across the organization.