Together we’re building a company that will endure and products people will love for generations to come.

We believe that people do their best in a culture that fosters inclusion, innovation, and success. Our values - Champion the Customer, Take the Lead, Run Together, Ack + Own and Bring Yourself - serve as the foundation of our collaborative and dynamic culture.

Whether it’s conducting a retrospective, participating in our bi-annual hack weeks, cranking out a new product feature, supporting our two PagerDuty bands, or doing our day-to-day work, Dutonians live and breathe these five values every day. Together, we solve real customer issues and fulfill our mission of connecting teams to real-time opportunities and elevate work to the outcomes that matter.

We’re building an inclusive workplace that represents the real, everyday people we support around the world. From how we develop our teams to who sits in the boardroom, we hope you can see yourself at PagerDuty.

Why We Need You:

We’re looking for a senior data engineer who can collaborate with data scientists and ML engineers to empower our AIOPs and other ML projects. If you are technical, creative, future-focused, and excited about fostering an environment amongst our engineers that helps create avenues for success and learning, then this is an excellent opportunity for you.

How you contribute to our vision: Key Responsibilities

• Collaborate with data scientists and ML engineers to accelerate the production of machine learning (ML) based solutions
• Drive and define standards for data engineering across your team 
• Provide guidance, technical leadership, and mentoring to other members of the team
• Proactively recommend improvements and new approaches addressing potential systemic pain points and technical debt
• Be curious and innovative to drive initiatives ensuring timely and continuous delivery 

Minimum Requirements

• You have 4+ years of experience building, designing, and evolving data architecture for large scale systems 
• Experience working with Product teams ensuring a timely delivery
• Have a deep understanding of the trade-offs to be considered when designing and delivering data platform based on the end-use case
• Demonstrated experience with data modeling, database design, extract transform load (ETL) processes, working with unstructured data, and cloud-based data infrastructure
• Awareness of and experience with ML processes, technologies, and infrastructure