Data Engineering Manager

Trupanion1; Seattle (WA); Hybrid; USD 180,000 - 230,000; Full time

Job summary

Trupanion1 is seeking a Data Engineering Manager in the Seattle area to lead a team delivering reliable data pipelines and analytics-ready data assets. The role partners with Data Analytics, Data Science, and business stakeholders to align engineering efforts with organizational goals.

You will oversee design and operation of enterprise data platforms on Microsoft Azure (Azure SQL, Synapse, ADLS Gen2, Databricks) and drive CI/CD, testing, and governance practices. A hybrid schedule is supported.

Qualifications

• 8+ years in data engineering with leadership or tech-lead responsibilities.

Responsibilities

• Lead Data Engineering team by setting priorities and supporting day-to-day execution.
• Collaborate with Data Analytics, Data Science, and stakeholders to align initiatives.
• Design, develop, and maintain scalable data pipelines (ETL/ELT) and data integrations.

Skills

Data engineering leadership

SQL proficiency

Python / PySpark

Azure cloud

CI/CD for data

Stakeholder collaboration

Education

Bachelor's degree in CS/Engineering

Tools

Azure Synapse

Azure SQL

Databricks

Spark

ADLS Gen2

SQL Server

Job description

The Data Engineering Manager is responsible for leading the Data Engineering team. The role coordinates team priorities, supports execution, ensures high-quality engineering practices, and aligns work with Data Analytics, Data Science, and other stakeholders. The manager helps maintain stable operations while driving improvements in processes, communication, and delivery.

This position is open to candidates in the Seattle, WA area. You will have a hybrid remote/in-office schedule where you will work from our casual, pet-friendly office at least 3 days a week.

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 across the organization.
• Partner closely with Data Analytics, Data Science, and business stakeholders to align data engineering initiatives with organizational goals and analytical needs.
• Ensure the design, development, and maintenance of scalable, high‑quality data pipelines, including ETL/ELT processes, data integrations, and 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 defined SLAs/SLOs.
• Establish and promote strong engineering standards and best practices, including code quality, testing, documentation, and operational excellence.
• Drive continuous improvement in engineering workflows, delivery processes, and team communication to increase efficiency and predictability.
• Oversee data warehousing and analytics solutions within a Microsoft Azure environment, including technologies such as Azure SQL, Synapse Analytics, ADLS Gen2, Databricks, and Spark.
• Ensure data accuracy, integrity, and compliance with enterprise standards and regulatory requirements, including SOX‑related controls where applicable.
• Manage software and infrastructure change processes, including release management, source control practices, and deployment coordination.
• Support Agile development practices by improving sprint execution, backlog management, and cross‑team collaboration.
• Identify technical risks and constraints early, elevate issues as needed, and guide the team toward optimal, pragmatic solutions.
• Mentor and develop data engineers by providing technical guidance, feedback, and growth opportunities to build a strong and resilient team.
• Contribute to technology and architecture decisions to support the long‑term evolution of the enterprise data ecosystem.

Qualifications

• Bachelor's level degree in Computer Science, Engineering, or appropriate work experience required
• 3+ years of experience leading, managing, or providing technical leadership to data engineering teams, including coaching engineers and managing delivery across multiple concurrent initiatives.
• 5+ years of experience designing, building, and enhancing ETL processes for data warehousing, data marts, data integrations, data analysis across the enterprise
• 3+ years of technical experience with data warehousing in a Microsoft/Azure environment with the following tools or similar: SQL Server, Azure SQL, Azure Data Lake Storage - ADLS Gen2, Azure Streaming Analytics, DataBricks, DeltaLake, Apache Spark/SparkSQL.
• Experience with similar tools in a Linux/AWS environment a plus.
• Experience required 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 technical depth in Python, PySpark, or similar languages to provide technical direction and conduct meaningful design/code reviews.

Skills

• Proven experience in leading, designing and implementing data pipelines for a variety of flows (data integrations across systems, ETL/ELT pipelines, streaming analytics, big data analytics)
• Familiar with data accuracy standards, and compliance expectations (including SOX‑related work).
• Experience with Microsoft Azure IaaS & PaaS solutions
• Experience driving towards Continuous Delivery while valuing and maintaining a strong attention to detail
• Experience with Agile software development organizations
• An ability to quickly identify and drive to the optimal solution when presented with a series of constraints
• Experience in improving engineering workflows, documentation, and reliabilit