Senior Manager of Data Engineering, Docker

• 8+ years of data engineering experience with 4+ years in technical leadership roles managing teams of 5+ engineers
• Proven track record building and scaling data engineering organizations at high-growth technology companies
• Strong people management skills including hiring, performance management, and career development
• Experience leading cross-functional initiatives involving Product, Engineering, and Business stakeholders
• Excellent communication skills with ability to influence executives and technical teams
• Deep hands-on experience with Snowflake including data warehousing, performance optimization, and cost management
• Proficiency with Apache Airflow for orchestrating complex data workflows and pipeline management
• Strong expertise in DBT (Data Build Tool) for data transformation, modeling, and testing
• Extensive AWS experience including data services (S3, Redshift, EMR, Glue, Lambda) and infrastructure management
• Experience with Sigma or similar modern BI platforms for self-service analytics and data visualization
• Strong background in data pipeline development, ETL/ELT processes, and streaming data architectures
• Proficiency with programming languages commonly used in data engineering (Python, SQL, Scala)
• Knowledge of infrastructure-as-code practices and modern DevOps tools
• Understanding of SaaS business models, product analytics, and customer lifecycle metrics
• Experience with customer-facing analytics and embedded reporting capabilities
• Knowledge of data privacy regulations, security frameworks, and enterprise compliance requirements
• Familiarity with developer tools and infrastructure software business models
• Experience supporting product launches with data infrastructure and analytics capabilities

Desirable

• Docker is seeking a Senior Manager to lead our Data Engineering team and drive the strategic evolution of data analytics across the company
• As Docker continues to expand our product portfolio and serve millions of developers and thousands of enterprise customers globally, we need a visionary technical leader who can build world-class data infrastructure and establish analytics capabilities that power product innovation, business intelligence, and customer insights
• This role combines technical leadership with people management to build a high-performing data engineering organization
• You'll be responsible for shaping Docker's data strategy, establishing scalable data pipelines and platforms, and enabling data-driven decision making across Product, Engineering, Sales, Marketing, and Executive teams
• You'll work closely with internal stakeholders and customers to understand data requirements and deliver analytics solutions that drive business outcomes
• Success in this role requires deep technical expertise in modern data platforms, strong leadership skills, and the ability to translate business needs into robust data solutions that scale with Docker's growth
• Build, lead, and scale a high-performing data engineering team of 8-12 engineers across data infrastructure, analytics, and business intelligence
• Establish hiring standards and recruit top-tier data engineering talent in a competitive market
• Foster a culture of technical excellence, innovation, and customer obsession within the data organization
• Mentor senior engineers and develop next-generation technical leadership within the data discipline
• Partner with HR and Engineering leadership on career development, performance management, and team growth
• Ensure team participation in the on-call rotation and step in as needed. Respond to incidents, troubleshoot production issues, and drive continuous improvement in system reliability
• Define and execute the long-term technical strategy for Docker's data platform, ensuring alignment with business objectives and product roadmap
• Architect and oversee development of scalable, reliable data infrastructure leveraging Snowflake as the core data warehouse and AWS cloud services
• Drive implementation of modern data orchestration using Airflow for workflow management and DBT for data transformation and modeling
• Lead technical decisions around data platform technologies, vendor selection, and build vs. buy strategies
• Establish data governance, security, and compliance frameworks to support enterprise customer requirements
• Oversee modernization of legacy data systems and migration to cloud-native data platforms
• Partner with Product Management teams to enable data-driven product development and feature validation
• Collaborate with Sales and Customer Success teams to deliver customer-facing analytics and reporting capabilities
• Support Marketing and Growth teams with user behavior analytics, funnel optimization, and campaign effectiveness measurement
• Work with Finance team to enable accurate business reporting, forecasting, and operational metrics
• Engage directly with enterprise customers to understand analytics requirements and deliver custom data solutions
• Establish self-service analytics capabilities using Sigma and other BI tools enabling teams across Docker to access and analyze data independently
• Build comprehensive dashboards and reporting systems for product metrics, business KPIs, and operational insights
• Implement advanced analytics capabilities including machine learning, predictive modeling, and anomaly detection
• Drive adoption of data visualization tools and establish best practices for analytics across the organization
• Lead data literacy initiatives and training programs to increase analytical capabilities company-wide
• Own the operational excellence of Docker's data platform including Snowflake performance optimization, Airflow pipeline reliability, and AWS cost management
• Establish comprehensive monitoring, alerting, and incident response procedures for data systems across the modern data stack
• Implement robust data quality frameworks and automated testing for DBT models, data pipelines, and analytics
• Drive cost optimization initiatives for Snowflake compute, AWS infrastructure, and analytics tools
• Ensure compliance with data privacy regulations (GDPR, CCPA) and enterprise security requirements
• Key Success Metrics
• Team performance and retention rates with strong employee satisfaction scores
• Successful delivery of data platform capabilities enabling new product features and business initiatives
• Improved data accessibility and self-service adoption across Docker teams using Sigma and other BI tools
• Customer satisfaction scores for data and analytics solutions
• Cost optimization and operational efficiency improvements for Snowflake, AWS, and data infrastructure
• Data quality and reliability metrics meeting enterprise SLA requirements
• Successful implementation and adoption of modern data stack (Snowflake + Airflow + DBT + Sigma)