Senior Software Engineer, Data Infrastructure

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.

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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.

Success requires strong data platform experience, solid system design skills, and the ability to guide technical direction while supporting team growth. You will play a key role in scaling Docker's data capabilities across our product portfolio.

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

• 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

Key Success Metrics

• Timely 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 without sacrificing performance

• Effective mentorship demonstrated through team skill growth and knowledge sharing

• Cross-functional stakeholder satisfaction with data platform capabilities and reliability

• Adoption of data engineering best practices and technical standards across the team

Impact You'll Make

As a Senior Software Engineer on our Data Infrastructure team, you will build the foundation powering Docker's product analytics and business intelligence. You will design and implement systems that allow teams across the company to make data-informed decisions while expanding Docker's enterprise capabilities.

You will solve challenging technical problems at scale, expand your technical leadership, and mentor fellow engineers. Your work will enable millions of developers to build software efficiently through data-driven insights.

Docker considers visa sponsorship on a case-by-case basis based on business needs.

Compensation & Equity

United States: $160,900 - $260,700 + equity

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Posting Information

• Open vacancy: This posting is for an existing open role.

• AI in hiring: Docker may use AI-assisted tools during our recruiting process.

• Interview recordings: Candidates will be invited to opt in to interview recordings to support interviewer calibration and consistent evaluations. Recordings are optional and require explicit consent.

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Perks & 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.

Docker is proud to be an equal opportunity employer. We are committed to building a team that reflects a broad range of backgrounds, experiences, and perspectives. We believe diverse teams build better products, make better decisions, and better serve our global community.