Senior Software Engineer, Data Infrastructure - Docker

About Docker

Docker has been one of the most loved brands in developer tooling, trusted by more than 20 million monthly users and over 20 billion container image pulls. From solo founders to the world's largest companies, developers rely on Docker to build, share, and run their applications across our suite of products including Docker Desktop, Docker Hub, and Docker Scout.

We are a globally distributed, remote-first team building the tools that define how software gets built and delivered. As AI agents redefine software development, Docker is at the center of that shift, providing the sandboxed environments, verified images, and secure infrastructure that make autonomous workflows trustworthy by default.

The Role

Docker is seeking a Senior Software Engineer to join our Data Infrastructure team and help build scalable data systems. You will design and launch infrastructure that enables analytics and data-driven decision-making across Product, Engineering, Sales, Marketing, Finance, and Executive teams.

This role combines individual technical contributions with system ownership and mentorship. You will design robust data pipelines, establish technical standards and best practices, and partner with cross-functional teams to deliver impactful data solutions.

Responsibilities

Technical Strategy & Architecture Leadership

• 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.

Hands-On Engineering & System Development

• 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.

Cross-Functional Collaboration & Requirements Engineering

• 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.

Technical Operations & Reliability

• 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.

Mentorship & Technical Leadership

• 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.

Qualifications

Required Technical Expertise

• 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.

System Design & Architecture

• 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.

Communication & Collaboration

• 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.

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 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.