Job description

Who we are

Adelaide is the leader in one of the fastest-growing areas of digital advertising: attention metric

s. Since 2020, we’ve been a trusted measurement partner for 40% of Fortune 50 companies. They rely on our metric, AU, to maximize the effectiveness of media spend. AU is “the attention economy's most widely recognized metric,” according to Adweek, and we swept the measurement category in the 2024 Adexchanger awards.

Position Overview

This position reports to the Senior Data Analytics Engineer; it will behave cross functionally across Data teams with an emphasis on supporting the Analytics team and their workflows.In this role, you will be joining a team of data scientists and engineers. You'll help build and maintain the semantic layer of our data pipeline, ensuring clean, consistent, and reusable data models. Day-to-day activities range from developing and testing data models, editing LookML, managing a data catalog, automating data analysis workflows, and working closely with stakeholders to understand and support their data needs.

What you'll learn

An important part of our culture is continuing education and the sharing of ideas. We offer:

A large network of investors and advisors for you to access that will help your team succeed
Mentorship from executives with decades of experience in adtech and media
Regular internal knowledge-sharing sessions
Education budget to accelerate your team’s development

Core responsibilities:

Assist in building, testing, and maintaining transformational data models (dbt, Redshift)
Help create and manage a clean, reliable reporting layer in Looker using LookML
Work cross-functionally with emphasized support to the Analytics Team
Help identify and automate manual data analysis processes (Python, dbt)
Contribute to maintaining a well-organized data catalog with accurate, accessible metadata for both technical and non-technical audiences
Ensure metric consistency across dashboards and data tools
Collaborate with analysts and PMs to document key metrics and definitions
Monitor adoption and identify opportunities for improved data usability

What you'll bring:

SQL Proficiency – Strong ability to write, optimize, and debug SQL queries. You also understand data modeling and warehouse best practices, and you’re committed to writing clean, readable, and well-documented code.
BI & Visualization Tools – Hands-on experience with BI/dashboarding platforms (e.g., Looker, Tableau).
Data Modeling Tools – Familiarity with semantic modeling tools (e.g., LookML) and dbt; understanding of ETL concepts. Experience with orchestration tools (e.g., Airflow) is a plus.
Data Quality & Testing – Strong attention to detail in building data validation, profiling routines, and root cause analysis workflows.
Programming Skills – Experience with Python for data transformation, scripting, and automation; experience with associated libraries (e.g., pandas, openpyxl) is a plus.
Communication & Collaboration – Excellent interpersonal skills with experience working cross-functionally; ability to translate technical concepts for non-technical audiences and deliver training/support.
Educational Background & Experience – Bachelor’s degree in a quantitative, technical, or analytical field (e.g., Computer Science, Math, Physics, Engineering) or a rigorous coding bootcamp with a portfolio demonstrating the above skills.

Required profile