Senior Software Engineer, Data Infrastructure

Docker, Inc

. Architect and implement key components of Docker’s data platform and drive technical direction .

Posted 9/16/2026full-timeRemote • Washington • United StatesSenior💰 $160,900 - $260,700 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building scalable data infrastructure and optimizing performance using Snowflake, AWS, and Apache Airflow. Proficient in data modeling with DBT and capable of translating business analytics requirements into technical solutions while ensuring data quality and governance.

Highest-signal resume keywords

Snowflake Performance OptimizationDBT Data ModelingApache Airflow Workflow OrchestrationAWS Data Services (S3, Redshift, EMR, Glue, Lambda, Kinesis)Data Governance and Compliance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonSQLData EngineeringData WarehousingDimensional ModelingStream ProcessingEvent-Driven ArchitecturesInfrastructure-as-CodeCI/CD PipelinesData Quality Checks

Soft Skills

Clear CommunicationCollaborationMentoringTechnical Leadership

Tools & Technologies

SnowflakeDBTApache AirflowSigmaAWSKubernetesGCPAzure

Certifications & Qualifications

Bachelor’s Degree in Computer ScienceAdvanced Degree in Computer Science or Data Engineering (Preferred)

Industry Keywords

Data GovernanceGDPRCCPABusiness IntelligenceEmbedded ReportingFinancial Data SystemsRevenue Analytics

Tech Stack

Tools & technologies

AirflowAmazon RedshiftApacheAWSAzureCloudDockerGoogle Cloud PlatformKubernetesPythonSQL

About the role

Key responsibilities & impact

Architect and implement key components of Docker’s data platform and drive technical direction
Design and build scalable data infrastructure using Snowflake, AWS, Airflow, DBT, and Sigma
Build end-to-end data pipelines for real-time and batch analytics across Docker’s product ecosystem
Evaluate data platform technologies, architectural patterns, and engineering best practices
Establish standards for data quality, testing, monitoring, and operational excellence
Build high-throughput data systems supporting high-volume user interactions
Develop DBT data transformations and models for analytics and business intelligence
Develop and maintain Apache Airflow orchestration workflows
Optimize Snowflake performance and cost efficiency
Build data APIs and services for self-service analytics and downstream integrations
Translate business and product analytics requirements into technical solutions
Collaborate with Data Scientists and Analysts on analytics, ML, and BI capabilities
Deliver accurate reporting and operational dashboards with Finance, Sales, and Marketing
Support customer-facing analytics and embedded reporting
Partner with Security and Compliance on data governance and regulatory compliance
Ensure reliability, monitoring, alerting, and incident response for owned components
Implement data quality checks and automated pipeline and transformation testing
Establish disaster recovery and business continuity procedures
Troubleshoot and resolve complex issues affecting data availability and accuracy
Mentor engineers on system design, technical execution, and data engineering practices
Conduct technical design reviews and provide architecture feedback
Share knowledge through documentation, tech talks, and cross-team collaboration
Participate in hiring and technical assessments for data engineering roles

Requirements

What you’ll need

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
Visa sponsorship considered case-by-case based on business needs
Preferred: experience at high-growth technology companies, particularly in developer tools or infrastructure software
Preferred: background with container technologies, Kubernetes, or cloud-native development
Preferred: knowledge of machine learning platforms and MLOps practices
Preferred: experience with GCP or Azure and multi-cloud data strategies
Preferred: familiarity with data catalog tools, metadata management, and data lineage systems
Preferred: advanced degree in Computer Science, Data Engineering, or a related technical field
Preferred: experience with customer-facing analytics and embedded reporting solutions
Preferred: knowledge of financial data systems and revenue analytics

Benefits

Comp & perks

Remote-first by design; work from home, with offices in Seattle and Paris for connection and collaboration
Flexible schedule
Generous PTO
Designated quarterly Whaleness Days
Designated end-of-year Whaleness break
Home office support
Technology stipend equivalent to US$100 net per month
Annual learning and development stipend for conferences, courses, certifications, and continued learning
16 weeks of paid parental leave after six months of employment
Equity for all full-time employees
Medical benefits
Retirement benefits
Paid holidays
Docker swag