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

• Implementing and owning ETL/ELT pipelines (both batch and streaming) in
  Cloud environment
• Collaborate with data scientists and ML engineers to accelerate the production
  of machine learning (ML) based solutions
• 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 3+ years of experience delivering Big data solutions 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 delivering data platform initiatives
• 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

Preferred Requirements

• Experience with AWS
• Experience with real time streaming
• Experience with building API first systems