PagerDuty, Inc.
(NYSE:PD) is a global leader in digital operations management.
Trusted by nearly half of both the Fortune 500 and the Forbes AI 50, as well as approximately two-thirds of the Fortune 100, PagerDuty is essential for delivering always-on digital experiences to modern businesses.
Join us.
At PagerDuty, you'll tackle complex problems, collaborate with kind and ambitious people, and help build a more equitable world—all in a flexible, award-winning workplace.
PagerDuty is growing, and we are looking for a Data Engineer III to join our global Enterprise Data team in IT to manage and contribute to the software and services that we provide to our users.
As a Data Engineer III, you will be responsible for designing, building, deploying, and supporting solutions for teams across PagerDuty's growing global user base.
Together with the other members of the Data Platform team, you will have the opportunity to redefine how PagerDuty designs, builds, integrates, and maintains a growing set of software and SaaS solutions.
In this role, you will be working cross-functionally with business domain experts, analytics, and engineering teams to re-design and re-implement our Data Warehouse model(s).
Key Responsibilities
- Translate business requirements into data models that are easy to understand and used by different disciplines across the company.
Design, implement, and build pipelines that deliver data with measurable quality under the SLA.
- Partner with business domain experts, data analysts, and engineering teams to build foundational data sets that are trusted, well understood, aligned with business strategy, and enable self-service.
- Be a champion of the overall strategy for data governance, security, privacy, quality, and retention that will satisfy business policies and requirements.
- Own and document foundational company metrics with a clear definition and data lineage.
- Identify, document, and promote best practices.
Contribute to the data ecosystem and build a strong data foundation for the company.
- Design, implement, and scale data pipelines that transform billions of records into actionable data models that enable data insights.
- Provide hands-on technical support to build trusted and reliable domain-specific datasets and metrics.
Basic Qualifications
- 5+ years of experience working in data integration, pipelines, and data modeling.
- Experience designing and deploying code in Data platforms in a cloud-based and Agile environment.
- Knowledge and experience of relational databases and being capable of writing complex SQL.
- Experience working with Cloud-based Data Warehousing Platform such as Snowflake or AWS, or Databricks.
- Experience with Python and SQL.
- Experience working with SQL, AWS S3, and ETL/Automation Workflow tools like Workato, etc.
Preferred Qualifications
- Bachelor's degree in Computer Science, Engineering, or related field, or equivalent training, fellowship, or work experience.
- Knowledge of at least one Data Visualization tool, like Tableau, Power BI, is preferred.
- Excellent written and verbal communication and interpersonal skills, able to effectively collaborate with technical and business partners.
- Smart Individual to pick up and jump into any new technology as and when needed, and a good team player.
- Hands-on knowledge and experience with Gen AI-related projects using some of the tools, like Snowflake, Databricks, is a big plus.
PagerDuty is a flexible, hybrid workplace.
We embrace and encourage in-person working as an integral part of our culture.
This role is expected to come into our Santiago office 2 days per week , so you can thrive in your new role and fully embrace being a Dutonian!
We encourage you to submit your resume even if you don't meet every requirement.
We value potential and consider each candidate's full professional story.
PagerDuty is an equal opportunity employer.
PagerDuty does not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, parental status, veteran status, or disability status.
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