Data Engineer

Fastly•San Francisco, CA

5h•Hybrid

About The Position

Fastly’s Analytics team empowers leaders across the business with actionable data to drive key decisions. The team is working to expand and mature a best-in-class internal data platform. As a Data Engineer, you will contribute to the evolution of our data infrastructure, optimizing pipelines on Google Cloud Platform (GCP), scaling ingestion of complex data sources, and following best practices for performance and reliability. This is an opportunity to contribute to impactful projects in a dynamic, collaborative, and innovative environment, supporting data scientists, data analysts and business analysts across the organization.

Requirements

3-5 years of hands-on experience in BI or Analytics teams.
Experience with cloud data warehouse technologies (BigQuery is a plus)
Driven by logical and analytical thinking to solve problems. And have an eye for detail-oriented analysis and design
Hands-on experience in building and managing data transformations (dbt experience is a plus)
Able to craft functional and user-friendly data products (data models, pipelines, etc..) for both technical and non-technical stakeholders
Experience with a data visualization tool (ie. Looker, Tableau, Mode)
A SQL power user, capable of complex, efficient, and logical SQL to accomplish data modeling goals
Functional knowledge of financial, sales, or other core business metrics
Able to manage professional relationships across a variety of stakeholders
Able to identify and prioritize multiple requirements and manage expectations accordingly
Collaborative and willing to traverse and contribute to the entire data stack, from front-end to back-end as needed

Responsibilities

Collaborate and partner with stakeholders, and decision-makers to prioritize, design, and fulfill their data analytics needs
Identify KPIs driving business growth and performance and work closely with the business teams to define them
Work across the data stack to design and build various data products (pipelines, data models, analysis) that enable the business to make data backed decisions
Help garner trust in data from the business by documenting analytics best practices, assisting with data discoverability, governance, and accuracy
Promote and drive a self-service data culture by building easy to use analytics products and teaching the technical and non-technical users how to use them
Communicate progress, risks, and completion of projects to stakeholders