Senior Manager, Data Engineering (West Coast Preferred)

Docker

2d•Remote

About The Position

At Docker, we make app development easier so developers can focus on what matters. Our remote-first team spans the globe, united by a passion for innovation and great developer experiences. With over 20 million monthly users and 20 billion image pulls, Docker is the #1 tool for building, sharing, and running apps—trusted by startups and Fortune 100s alike. We’re growing fast and just getting started. Come join us for a whale of a ride! 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.

Requirements

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

Nice To Haves

Experience at developer tools, infrastructure software, or container technology companies
Background in platform engineering or developer experience roles
Experience with machine learning platforms and MLOps practices using AWS SageMaker or similar
Knowledge of container technologies, Kubernetes, or cloud-native development
Advanced degree in Computer Science, Data Science, or related technical field
Experience with real-time analytics and event-driven architectures
Familiarity with modern data catalog tools and metadata management
Experience with additional cloud data warehouses (BigQuery, Databricks) for multi-cloud strategies

Responsibilities

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

Benefits

Freedom & flexibility; fit your work around your life
Designated quarterly Whaleness Days plus end of year Whaleness break
Home office setup; we want you comfortable while you work
16 weeks of paid Parental leave
Technology stipend equivalent to $100 net/month
PTO plan that encourages you to take time to do the things you enjoy
Training stipend for conferences, courses and classes
Equity; we are a growing start-up and want all employees to have a share in the success of the company
Docker Swag
Medical benefits, retirement and holidays vary by country
Remote-first culture, with offices in Seattle and Paris