Original listing text, shown exactly as published by the company.
Responsibilities
Technical Strategy & Architecture Leadership
- Define and drive the technical strategy for Docker's data platform architecture, establishing long-term vision for scalable data systems
- Lead design and implementation of highly scalable data infrastructure leveraging Snowflake, AWS, Airflow, DBT, and Sigma
- Architect end-to-end data pipelines supporting real-time and batch analytics across Docker's product ecosystem
- Drive technical decision-making around data platform technologies, architectural patterns, and engineering best practices
- Establish technical standards for data quality, testing, monitoring, and operational excellence
Hands-On Engineering & System Development
- Design and build robust, scalable data systems that process petabytes of data and support millions of user interactions
- Implement complex data transformations and modeling using DBT for analytics and business intelligence use cases
- Develop and maintain sophisticated data orchestration workflows using Apache Airflow
- Optimize Snowflake performance and cost efficiency while ensuring reliability and scalability
- Build data APIs and services that enable self-service analytics and integration with downstream systems
Cross-Functional Collaboration & Requirements Engineering
- Partner with Product, Engineering, and Business teams to understand analytics requirements and translate them into technical solutions
- Collaborate with Data Scientists and Analysts to enable advanced analytics, machine learning, and business intelligence capabilities
- Work with Finance, Sales, and Marketing teams to deliver accurate reporting and operational dashboards
- Support customer-facing analytics initiatives and embedded reporting capabilities
- Engage with Security and Compliance teams to ensure data governance and regulatory requirements are met
Technical Operations & Reliability
- Own operational excellence for critical data systems including monitoring, alerting, and incident response
- Implement comprehensive data quality frameworks and automated testing for data pipelines and transformations
- Drive performance optimization and cost management initiatives across the data platform
- Establish disaster recovery and business continuity procedures for business-critical data systems
- Lead troubleshooting and resolution of complex technical issues affecting data availability and accuracy
Mentorship & Technical Leadership
- Mentor junior and mid-level engineers on technical skills, system design, and data engineering best practices
- Conduct technical design reviews and provide guidance on architectural decisions
- Drive knowledge sharing initiatives including documentation, tech talks, and cross-team collaboration
- Establish and promote engineering excellence practices across the data organization
- Contribute to hiring and technical assessment processes for data engineering roles
- This role may require participation in an on-call rotation to provide support outside of standard business hours, including evenings, weekends, and holidays, as needed.
Required Qualifications
Technical Expertise
- 8+ years of software engineering experience with 3+ years focused on data engineering and analytics systems
- Expert-level experience with Snowflake including advanced SQL, performance optimization, and cost management
- Deep proficiency in DBT for data modeling, transformation, and testing with experience in large-scale implementations
- Strong expertise with Apache Airflow for complex workflow orchestration and pipeline management
- Hands-on experience with Sigma or similar modern BI platforms for self-service analytics
- Extensive AWS experience including data services (S3, Redshift, EMR, Glue, Lambda, Kinesis) and infrastructure management
- Proficiency in Python, SQL, and other programming languages commonly used in data engineering
- Experience with infrastructure-as-code, CI/CD practices, and modern DevOps tools
- Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent practical experience
System Design & Architecture
- Proven track record designing and implementing large-scale distributed data systems
- Deep understanding of data warehousing concepts, dimensional modeling, and analytics architectures
- Experience with stream processing, event-driven architectures, and real-time data systems
- Knowledge of data governance, security frameworks, and compliance requirements (GDPR, CCPA)
- Strong background in performance optimization and cost management for cloud data platforms
Leadership & Collaboration
- Demonstrated ability to drive technical strategy and influence engineering decisions across teams
- Experience mentoring engineers and leading technical initiatives without direct management authority
- Excellent communication skills with ability to explain complex technical concepts to diverse audiences
- Track record of successful cross-functional collaboration with Product, Business, and Executive stakeholders
- Experience establishing technical standards and driving adoption across engineering organizations
Preferred Qualifications
- Experience at high-growth technology companies, particularly in developer tools or infrastructure software
- Background with container technologies, Kubernetes, or cloud-native development
- Knowledge of machine learning platforms and MLOps practices
- Experience with additional cloud platforms (GCP, Azure) and multi-cloud data strategies
- Familiarity with modern data catalog tools, metadata management, and data lineage systems
- Advanced degree in Computer Science, Data Engineering, or related technical field
- Experience with customer-facing analytics and embedded reporting solutions
- Knowledge of financial data systems and revenue analytics
Key Success Metrics
- Successful design and delivery of scalable data systems supporting company-wide analytics needs
- System reliability and performance metrics meeting enterprise SLA requirements
- Cost optimization achievements for data infrastructure while maintaining performance
- Technical mentorship effectiveness measured by team growth and knowledge transfer
- Cross-functional stakeholder satisfaction with data platform capabilities and reliability
- Contribution to data engineering best practices and technical standards adoption
Impact You'll MakeAs a Staff Software Engineer in our Data group, you'll be instrumental in building the data foundation that powers Docker's product innovation and business intelligence. You'll architect and implement systems that enable teams across Docker to make data-driven decisions while creating analytics capabilities that differentiate our products in the market. Your technical leadership will be critical to scaling Docker's data infrastructure as we continue to expand our product portfolio and serve enterprise customers globally.
You'll have the opportunity to work on challenging technical problems at scale while directly influencing Docker's data strategy and mentoring the next generation of data engineers. Your contributions will enable millions of developers to build better software through the insights and capabilities your data systems provide.
Docker considers visa sponsorship on a case-by-case basis based on business needs.
