Data Engineer, Advertising Measurement Attribution

About this role

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

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

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

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Develop sophisticated SQL transformations across large analytical datasets.

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

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

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

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

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

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

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Analyze relationships between media exposure and downstream outcomes to support measurement and optimization.

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Work with datasets at different grains and across different identifiers, including event-level and impression-level data.

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Reconcile multiple sources representing similar business events and establish clear approaches when sources disagree.

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Partner with analytics, media, technology, and other agency teams to ensure data products answer the right business questions.

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Use AI-assisted development tools thoughtfully to explore solutions, debug issues, generate documentation and test cases, and identify potential edge cases.

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Review and validate AI-generated outputs rather than treating them as inherently correct.

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Document technical decisions, assumptions, dependencies, and data models so others can understand and confidently use what you build.

The data products you create will help our teams answer questions such as:

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Which media exposures are associated with conversions?

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How should conversions be attributed to media activity?

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How did a campaign perform across channels, placements, or audiences?

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What happened after a user or household was exposed to an advertisement?

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How should data from different measurement systems be reconciled?

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Which datasets should be considered authoritative when sources disagree?

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How should high-volume event and exposure data be modeled so analysts can efficiently uncover meaningful insights?

WHAT YOU’LL BRING

Experience & Technical Expertise

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3+ years of experience in data engineering, analytics engineering, data analytics, or a related technical discipline.

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Advanced SQL skills and the ability to reason through complex analytical data problems.

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Strong understanding of relational databases, analytical data warehouses, and data-modeling concepts.

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Experience designing analytical data models around business questions and downstream use cases.

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Experience working with large, complex datasets from multiple sources.

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Practical experience with Snowflake or a comparable modern cloud data warehouse.

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Experience building SQL-based transformations and data workflows using dbt or a comparable framework.

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Working knowledge of Python for data processing, pipeline development, automation, or analytical workflows.

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Experience with Git, code review, testing, and other software-development fundamentals.

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Experience working with APIs, JSON, or other semi-structured data.

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Understanding of data-quality testing, validation, and monitoring practices.

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Experience with BI, reporting, or analytical tools and an understanding of how downstream teams consume modeled data.

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Advertising, media, marketing, attribution, or measurement experience is strongly preferred.

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Experience with event-level or impression-level datasets is particularly valuable.

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Experience joining datasets with different grains, keys, and identifiers is highly valuable.

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Familiarity with advertising attribution or measurement methodologies is a plus.

Capabilities

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Strong analytical thinking with the ability to break complex or ambiguous problems into clear, solvable components.

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Ability to identify assumptions, unanswered questions, dependencies, and potential data-quality risks before they become…