Staff Software Engineer, Data Engineering

Docker, Inc is a leading company in developer tooling, trusted by millions of users. They are seeking a Staff Software Engineer to join their Data Engineering team and lead the development of scalable data infrastructure that supports analytics across the company.

Responsibilities:

• 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
• 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
• 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
• 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
• 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
• Take part in on-call rotation for your team; respond to incidents, debug production issues, and drive continuous improvement of system reliability

Requirements:

• 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
• 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
• 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
• 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