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Salary: USD 160,900 - 260,700 / annual
Docker is seeking a Senior Software Engineer to join its Data Infrastructure team, responsible for designing and building scalable data systems that enable analytics and data-driven decision-making across Product, Engineering, Sales, Marketing, Finance, and Executive teams.
You will combine individual technical contributions with system ownership and mentorship. Key responsibilities include architecting and implementing core components of Docker's data platform using Snowflake, AWS, Airflow, DBT, and Sigma; designing and building scalable data infrastructure; developing end-to-end data pipelines supporting real-time and batch analytics; and evaluating data platform technologies and architectural patterns.
On the hands-on engineering side, you will build high-throughput data systems, develop data transformations and models using DBT, maintain data orchestration workflows with Apache Airflow, optimize Snowflake performance and cost efficiency, and build data APIs enabling self-service analytics. You will translate business and product analytics requirements into technical solutions, collaborate with Data Scientists and Analysts, work with Finance, Sales, and Marketing teams on reporting and dashboards, and partner with Security and Compliance on data governance.
You will ensure system reliability through monitoring, alerting, and incident response; implement data quality checks and automated testing; optimize performance and manage infrastructure costs; establish disaster recovery procedures; and lead troubleshooting of complex technical issues. Additionally, you will mentor engineers on system design and data engineering best practices, conduct technical design reviews, contribute to knowledge sharing, and participate in hiring for data engineering roles.
Docker is a globally distributed, remote-first team building developer tools trusted by 20+ million monthly users. As AI agents redefine software development, Docker is at the center of that shift, providing sandboxed environments, verified images, and secure infrastructure for trustworthy autonomous workflows.
REQUIREMENTS:
Required:
- 6+ years of software engineering experience, with 3+ years focused on data engineering
- Bachelor's degree in Computer Science, Engineering, or 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 (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 for technical and non-technical stakeholders
- Proven ability to collaborate effectively with Product, Business, and Engineering partners
Preferred:
- 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
