Note: The job is a remote job and is open to candidates in USA. Docker, Inc. is a developer tooling company providing products and infrastructure for building, sharing, and running applications. Docker is seeking a Senior Software Engineer to design and build scalable data infrastructure, pipelines, APIs, and analytics systems while establishing technical standards, ensuring reliability, and mentoring engineers. The role partners with cross-functional teams to enable data-driven decision-making, advanced analytics, business intelligence, and reporting.
Responsibilities
- Architect and implement key components of Docker's data platform, driving technical direction for the team
- Design and build scalable data infrastructure leveraging Snowflake, AWS, Airflow, DBT, and Sigma
- Build end-to-end data pipelines supporting real-time and batch analytics across the product ecosystem
- Evaluate and select data platform technologies, architectural patterns, and engineering best practices
- Apply and help improve technical standards for data quality, testing, monitoring, and operational excellence
- Build high-throughput data systems supporting high-volume user interactions
- Develop data transformations and models using DBT for analytics and business intelligence
- Develop and maintain data orchestration workflows using Apache Airflow
- Optimize Snowflake performance and cost efficiency while ensuring reliability and scalability
- Build data APIs and services enabling self-service analytics and downstream integrations
- Translate business and product analytics requirements into technical solutions
- Collaborate with Data Scientists and Analysts to enable advanced analytics, ML, and BI capabilities
- Work with Finance, Sales, and Marketing teams to deliver accurate reporting and operational dashboards
- Support customer-facing analytics initiatives and embedded reporting capabilities
- Partner with Security and Compliance to ensure data governance and regulatory compliance
- Ensure system reliability, monitoring, alerting, and incident response for owned components
- Implement data quality checks and automated testing for data pipelines and transformations
- Optimize performance and manage infrastructure costs across the data platform
- Establish disaster recovery and business continuity procedures for critical data systems
- Lead troubleshooting and resolution of complex technical issues affecting data availability and accuracy
- Mentor engineers on system design, technical execution, and data engineering best practices
- Conduct technical design reviews and provide actionable feedback on software architecture
- Contribute to knowledge sharing through documentation, tech talks, and cross-team collaboration
- Model engineering excellence practices within the data team
- Participate in hiring and technical assessment processes for data engineering roles
Skills
- 6+ years of software engineering experience, with 3+ years focused on data engineering
- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience
- Strong experience with Snowflake, including SQL tuning, performance optimization, and cost management
- Proficiency with DBT for data modeling, transformation, and testing at production scale
- Experience orchestrating workflows and pipelines with Apache Airflow
- Experience using Sigma or similar modern BI platforms for self-service analytics
- Production experience with AWS data services (S3, Redshift, EMR, Glue, Lambda, Kinesis)
- Proficiency in Python and SQL for data engineering applications
- Experience with Infrastructure-as-Code, CI/CD pipelines, and modern DevOps practices
- Track record of designing and building large-scale distributed data systems
- Solid understanding of data warehousing, dimensional modeling, and analytics architectures
- Experience with stream processing, event-driven architectures, and real-time data systems
- Understanding of data governance, security standards, and privacy frameworks (e.g., GDPR, CCPA)
- Proven track record optimizing performance and cost for cloud data infrastructure
- Ability to guide technical choices through sound engineering judgment
- Experience mentoring engineers and leading technical projects without direct management authority
- Clear written and verbal communication skills, tailored to technical and non-technical stakeholders
- Proven ability to collaborate effectively with Product, Business, and Engineering partners
- 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 such as GCP or 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 a related technical field
- Experience with customer-facing analytics and embedded reporting solutions
- Knowledge of financial data systems and revenue analytics
Benefits
- Remote-first by design – Work from your home, with offices in Seattle and Paris for connection and collaboration.
- Flexibility that fits your life – We trust you to manage your schedule while delivering great work.
- Time to recharge – Generous PTO, designated quarterly Whaleness Days, and a designated end-of-year Whaleness break.
- Home office support – Set up your workspace for comfort and success.
- Technology stipend – Equivalent to US$100 net per month to help support your work.
- Learning & development – Annual stipend for conferences, courses, certifications, and continued learning.
- Parental leave – 16 weeks of paid parental leave after six months of employment.
- Equity for all full-time employees – Share in Docker's long-term success as we continue to grow.
- Comprehensive benefits – Medical, retirement, and paid holidays vary by country.
- Docker swag – Because representing the whale never gets old.
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