Data Engineering Manager

Trupanion•Seattle, WA

2d•$130,000 - $140,000•Hybrid

About The Position

The Data Engineering Manager leads the Data Engineering team, coordinates priorities, supports execution, maintains high-quality engineering practices, and aligns work with Data Analytics, Data Science, and other stakeholders. The role helps sustain reliable operations while improving processes, communication, and delivery. The position is open to candidates in the Seattle, Washington area and follows a hybrid remote/in-office schedule, with at least three days per week in Trupanion’s casual, pet-friendly office. The manager oversees data engineering work across scalable data pipelines, enterprise data platforms, data warehousing, and analytics solutions in a Microsoft Azure environment. They also guide team development, contribute to architecture decisions, and ensure adherence to engineering, operational, governance, and regulatory standards. The base salary range is $130,000–$140,000 per year on a full-time schedule. Employees are currently eligible for monthly bonuses and receive Restricted Stock Units as new-hire grants that vest over four years.

Requirements

Bachelor’s-level degree in Computer Science or Engineering, or appropriate work experience.
At least 3 years of experience leading, managing, or providing technical leadership to data engineering teams, including coaching engineers and managing delivery across concurrent initiatives.
At least 5 years of experience designing, building, and enhancing ETL processes for data warehousing, data marts, data integrations, and enterprise data analysis.
At least 3 years of technical experience with data warehousing in a Microsoft/Azure environment using tools such as SQL Server, Azure SQL, ADLS Gen2, Azure Streaming Analytics, Databricks, Delta Lake, Apache Spark, or Spark SQL.
Experience with software and infrastructure change management, release management, and source code control.
Experience operating production data platforms with defined SLAs, monitoring, incident management, root-cause analysis, and performance optimization.
Experience implementing CI/CD and automated testing practices for data engineering workloads.
Experience with modern data architecture patterns, including Lakehouse, medallion architecture, dimensional modeling, data warehousing, and batch and real-time processing.
Experience with cloud cost optimization and capacity management for enterprise data platforms.
Strong understanding of data governance, security, privacy, metadata, lineage, and role-based access control.
Strong SQL skills and sufficient hands-on depth in Python, PySpark, or similar languages to provide technical direction and conduct design and code reviews.
Proven experience leading, designing, and implementing data pipelines for system integrations, ETL/ELT, streaming analytics, and big data analytics.
Familiarity with data accuracy standards and compliance expectations, including SOX-related work.
Experience with Microsoft Azure IaaS and PaaS solutions.
Experience driving continuous delivery while maintaining strong attention to detail.
Experience in Agile software development organizations.
Ability to identify and drive toward optimal solutions when presented with constraints.
Experience improving engineering workflows, documentation, and reliability, and identifying and escalating risks early.
Must be authorized to work for any employer in the United States; the company cannot sponsor or take over sponsorship of an employment visa.

Nice To Haves

Experience with similar data engineering tools in a Linux/AWS environment.

Responsibilities

Lead and manage the Data Engineering team by setting priorities, assigning work, and supporting day-to-day execution to ensure reliable and timely data delivery.
Partner with Data Analytics, Data Science, and business stakeholders to align engineering initiatives with organizational goals and analytical needs.
Ensure the design, development, and maintenance of scalable data pipelines, including ETL/ELT processes, data integrations, streaming, and big data solutions.
Own the operational health of enterprise data platforms and pipelines, including availability, performance, data freshness, incident response, root-cause analysis, capacity planning, and adherence to SLAs/SLOs.
Establish and promote engineering standards and best practices for code quality, testing, documentation, and operational excellence.
Improve engineering workflows, delivery processes, and team communication to increase efficiency and predictability.
Oversee data warehousing and analytics solutions in a Microsoft Azure environment, including Azure SQL, Synapse Analytics, ADLS Gen2, Databricks, and Spark.
Ensure data accuracy, integrity, and compliance with enterprise standards and regulatory requirements, including applicable SOX-related controls.
Manage software and infrastructure change processes, including release management, source control, and deployment coordination.
Support Agile practices by improving sprint execution, backlog management, and cross-team collaboration.
Identify technical risks and constraints early, escalate issues as needed, and guide the team toward pragmatic solutions.
Mentor and develop data engineers through technical guidance, feedback, and growth opportunities.
Contribute to technology and architecture decisions supporting the long-term evolution of the enterprise data ecosystem.