Manager, Data Engineering Operations

Responsibilities

• Lead, mentor, and develop engineers on the Data Operations team, including hiring, performance management, and career development as needed
• Foster a collaborative, high-accountability team culture focused on quality, continuous improvement, and operational efficiency
• Manage team capacity, workload, and prioritization across competing demands
• Own incident management and resolution for pipeline failures, cluster issues, and data quality problems, driving thorough root cause analysis and lasting preventative improvements
• Define and enforce operational standards, runbooks, and on-call practices for the team
• Manage and maintain Databricks Workflows and job orchestration, ensuring SLAs are consistently met
• Establish and report on operational KPIs (uptime, pipeline reliability, incident response, cost) to leadership and stakeholders
• Set technical direction for the data platform in partnership with senior engineering leadership
• Drive platform reliability, performance, and cost-efficiency initiatives
• Partner with IT, Security, and Engineering teams on platform architecture, governance, and integration decisions
• Champion engineering best practices including code review, testing, observability, and documentation
• Communicate operational status, risks, and roadmap clearly to both technical and non-technical audiences
• Build strong working relationships with downstream consumers, ensuring the platform meets their needs

Qualifications

• 8+ years of hands-on data engineering experience, with at least 2 years in a leadership or management role
• Strong hands-on background with SQL development and relational/non-relational databases, including data modeling, schema design, and query optimization
• Professional experience developing scalable solutions in Python or a similar object-oriented language
• Proficiency in Databricks, Spark, and Delta tables; experience operating large-scale distributed data processing in production
• Demonstrated experience operating and monitoring data pipelines at scale, including incident response and on-call leadership
• Solid understanding of Lakehouse and Medallion architectures
• Experience with Azure data services (ADLS Gen2, Azure Data Factory, Event Hubs, or equivalent)
• Familiarity with Unity Catalog for data governance, access control, and data lineage
• Proven experience designing and maintaining data integration/ETL pipelines using Azure Data Factory or equivalent