Data Engineer

Technology & Analytics | The Builders and Translators

We are a 1,000-piece puzzle. Each of us is essential, interconnected, and intentionally designed. Together, we create something powerful: media that matters, growth that lasts, and a culture we are proud to build.

As part of our Technology & Analytics team, our Builders and Translators architect the systems and models that turn raw data into action and innovation. We unify, activate, and illuminate performance so the entire agency can think smarter, make better decisions, and move faster.

WHAT YOU’LL DO

As a Data Engineer focused on Advertising Measurement & Attribution, you will build the data infrastructure and analytical models that help us understand how media exposure translates into measurable business outcomes.

You will work across large, complex datasets from multiple sources, designing intentional data models that enable measurement, attribution, reporting, and media optimization. This role requires more than executing predefined transformations. You will need to understand the analytical question behind the data, work through ambiguity, identify the right assumptions and business rules, and translate complex information into reliable datasets that teams can use to make better decisions.

Strong SQL and analytical reasoning are at the center of this role. You will also work extensively with Snowflake and dbt while using modern AI-assisted development tools to accelerate research, implementation, debugging, testing, documentation, and analysis. AI can accelerate the work, but you remain accountable for understanding, validating, and ensuring the quality of what you build.

Key Responsibilities

• Build and maintain data models that support advertising measurement, attribution, reporting, and media optimization.

• Transform, integrate, and reconcile data from multiple platforms, systems, and measurement sources.

• Develop sophisticated SQL transformations across large analytical datasets.

• Build and maintain dbt models, tests, documentation, dependencies, and data-quality controls.

• Use Snowflake to store, transform, analyze, and serve analytical data at scale.

• Design data models that make complex source data understandable, efficient, and actionable for downstream users.

• Translate business, media, and measurement requirements into concrete datasets, definitions, and transformations.

• Investigate ambiguous data challenges and determine appropriate assumptions, definitions, joins, business rules, and modeling approaches.

• Diagnose unexpected data and determine whether issues originate from source data, business logic, transformations, joins, or model design.