Senior Manager, Data Engineering (West Coast Preferred)

At Docker, we make app development easier so developers can focus on what matters. Our remote-first team spans the globe, united by a passion for innovation and great developer experiences. With over 20 million monthly users and 20 billion image pulls, Docker is the #1 tool for building, sharing, and running apps—trusted by startups and Fortune 100s alike. We’re growing fast and just getting started. Come join us for a whale of a ride! Docker is seeking a

Senior Manager

to lead our Data Engineering team and drive the strategic evolution of data analytics across the company. As Docker continues to expand our product portfolio and serve millions of developers and thousands of enterprise customers globally, we need a visionary technical leader who can build world‑class data infrastructure and establish analytics capabilities that power product innovation, business intelligence, and customer insights. This role combines technical leadership with people management to build a high‑performing data engineering organization. You'll be responsible for shaping Docker's data strategy, establishing scalable data pipelines and platforms, and enabling data‑driven decision making across Product, Engineering, Sales, Marketing, and Executive teams. You'll work closely with internal stakeholders and customers to understand data requirements and deliver analytics solutions that drive business outcomes. Success in this role requires deep technical expertise in modern data platforms, strong leadership skills, and the ability to translate business needs into robust data solutions that scale with Docker's growth. Responsibilities

Team Leadership & Development Build, lead, and scale a high‑performing data engineering team of 8-12 engineers across data infrastructure, analytics, and business intelligence

Establish hiring standards and recruit top‑tier data engineering talent in a competitive market

Foster a culture of technical excellence, innovation, and customer obsession within the data organization

Mentor senior engineers and develop next‑generation technical leadership within the data discipline

Partner with HR and Engineering leadership on career development, performance management, and team growth

Ensure team participation in the on‑call rotation and step in as needed. Respond to incidents, troubleshoot production issues, and drive continuous improvement in system reliability.

Data Platform Strategy & Architecture Define and execute the long‑term technical strategy for Docker's data platform, ensuring alignment with business objectives and product roadmap

Architect and oversee development of scalable, reliable data infrastructure leveraging Snowflake as the core data warehouse and AWS cloud services

Drive implementation of modern data orchestration using Airflow for workflow management and DBT for data transformation and modeling

Lead technical decisions around data platform technologies, vendor selection, and build vs. buy strategies

Establish data governance, security, and compliance frameworks to support enterprise customer requirements

Oversee modernization of legacy data systems and migration to cloud‑native data platforms

Cross‑Functional Partnership & Customer Success Partner with Product Management teams to enable data‑driven product development and feature validation

Collaborate with Sales and Customer Success teams to deliver customer‑facing analytics and reporting capabilities

Support Marketing and Growth teams with user behavior analytics, funnel optimization, and campaign effectiveness measurement

Work with Finance team to enable accurate business reporting, forecasting, and operational metrics

Engage directly with enterprise customers to understand analytics requirements and deliver custom data solutions

Business Intelligence & Analytics Enablement Establish self‑service analytics capabilities using Sigma and other BI tools enabling teams across Docker to access and analyze data independently

Build comprehensive dashboards and reporting systems for product metrics, business KPIs, and operational insights

Implement advanced analytics capabilities including machine learning, predictive modeling, and anomaly detection

Drive adoption of data visualization tools and establish best practices for analytics across the organization

Lead data literacy initiatives and training programs to increase analytical capabilities company‑wide

Data Infrastructure & Operations Own the operational excellence of Docker's data platform including Snowflake performance optimization, Airflow pipeline reliability, and AWS cost management

Establish comprehensive monitoring, alerting, and incident response procedures for data systems across the modern data stack

Implement robust data quality frameworks and automated testing for DBT models, data pipelines, and analytics

Drive cost optimization initiatives for Snowflake compute, AWS infrastructure, and analytics tools

Ensure compliance with data privacy regulations (GDPR, CCPA) and enterprise security requirements

Qualifications

Required

Leadership & Management 8+ years of data engineering experience with 4+ years in technical leadership roles managing teams of 5+ engineers

Proven track record building and scaling data engineering organizations at high‑growth technology companies

Strong people management skills including hiring, performance management, and career development

Experience leading cross‑functional initiatives involving Product, Engineering, and Business stakeholders

Excellent communication skills with ability to influence executives and technical teams

Technical Expertise Deep hands‑on experience with Snowflake

including data warehousing, performance optimization, and cost management

Proficiency with Apache Airflow

for orchestrating complex data workflows and pipeline management

Strong expertise in DBT (Data Build Tool)

for data transformation, modeling, and testing

Extensive AWS experience

including data services (S3, Redshift, EMR, Glue, Lambda) and infrastructure management

Experience with Sigma

or similar modern BI platforms for self‑service analytics and data visualization

Strong background in data pipeline development, ETL/ELT processes, and streaming data architectures

Proficiency with programming languages commonly used in data engineering (Python, SQL, Scala)

Knowledge of infrastructure‑as‑code practices and modern DevOps tools

Business & Domain Knowledge Understanding of SaaS business models, product analytics, and customer lifecycle metrics

Experience with customer‑facing analytics and embedded reporting capabilities

Knowledge of data privacy regulations, security frameworks, and enterprise compliance requirements

Familiarity with developer tools and infrastructure software business models

Experience supporting product launches with data infrastructure and analytics capabilities

Preferred

Experience at developer tools, infrastructure software, or container technology companies

Background in platform engineering or developer experience roles

Experience with machine learning platforms and MLOps practices using AWS SageMaker or similar

Knowledge of container technologies, Kubernetes, or cloud‑native development

Advanced degree in Computer Science, Data Science, or related technical field

Experience with real‑time analytics and event‑driven architectures

Familiarity with modern data catalog tools and metadata management

Experience with additional cloud data warehouses (BigQuery, Databricks) for multi‑cloud strategies

Key Success Metrics

Team performance and retention rates with strong employee satisfaction scores

Successful delivery of data platform capabilities enabling new product features and business initiatives

Improved data accessibility and self‑service adoption across Docker teams using Sigma and other BI tools

Customer satisfaction scores for data and analytics solutions

Cost optimization and operational efficiency improvements for Snowflake, AWS, and data infrastructure

Data quality and reliability metrics meeting enterprise SLA requirements

Successful implementation and adoption of modern data stack (Snowflake + Airflow + DBT + Sigma)

Docker considers sponsorship on a case‑by‑case basis based on business needs. We use Covey as part of our hiring and / or promotional process for jobs in NYC and certain features may qualify it as an AEDT. As part of the evaluation process we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound on April 13, 2024. Please see the independent bias audit report covering our use of Covey here. Perks Freedom & flexibility; fit your work around your life

Designated quarterly Whaleness Days plus end of year Whaleness break

Home office setup; we want you comfortable while you work

16 weeks of paid Parental leave

Technology stipend equivalent to $100 net/month

PTO plan that encourages you to take time to do the things you enjoy

Training stipend for conferences, courses and classes

Equity; we are a growing start‑up and want all employees to have a share in the success of the company

Docker Swag

Medical benefits, retirement and holidays vary by country

Remote‑first culture, with offices in Seattle and ...