Data Engineer, Advertising Measurement Attribution

Attention Arc•Irvington, NY

8h•$90,000 - $100,000•Hybrid

About The Position

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. 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.

Requirements

3+ years of experience in data engineering, analytics engineering, data analytics, or a related technical discipline.
Advanced SQL skills and the ability to reason through complex analytical data problems.
Strong understanding of relational databases, analytical data warehouses, and data-modeling concepts.
Experience designing analytical data models around business questions and downstream use cases.
Experience working with large, complex datasets from multiple sources.
Practical experience with Snowflake or a comparable modern cloud data warehouse.
Experience building SQL-based transformations and data workflows using dbt or a comparable framework.
Working knowledge of Python for data processing, pipeline development, automation, or analytical workflows.
Experience with Git, code review, testing, and other software-development fundamentals.
Experience working with APIs, JSON, or other semi-structured data.
Understanding of data-quality testing, validation, and monitoring practices.
Experience with BI, reporting, or analytical tools and an understanding of how downstream teams consume modeled data.
Strong analytical thinking with the ability to break complex or ambiguous problems into clear, solvable components.
Ability to identify assumptions, unanswered questions, dependencies, and potential data-quality risks before they become downstream issues.
Sound judgment when determining how data should be modeled, reconciled, and interpreted.
Ability to understand the business question behind a technical request rather than simply implementing instructions.
Clear communication skills and the ability to explain technical decisions to both technical and non-technical stakeholders.
Strong organization and attention to detail, particularly when working with complex dependencies and large datasets.
Comfort working independently while knowing when to bring others into the problem.
Curiosity about unfamiliar technologies, datasets, business questions, and alternative approaches.
A mindset of continuous testing, learning, and optimization.
Credibility | You apply strong craft and sound judgment to your work, using data, insight, and expertise to produce reliable, high-quality outcomes. You investigate the why behind the data and build solutions people can trust.
Clarity | You organize technical thinking, documentation, and outputs in a way that makes complex information easy to understand and act on. You make assumptions, definitions, and tradeoffs visible.
Connection | You work effectively across teams and disciplines, translating business needs into technical solutions and contributing to shared outcomes through alignment, transparency, and collaboration.
Care | You demonstrate accountability and professionalism, recognizing that the quality and integrity of the data you build directly affects clients, colleagues, and business decisions.
Culture & Character | You take ownership of your growth, approach challenges with curiosity, use new technology thoughtfully, and contribute positively to the standards and expectations of the team.
You must be authorized to work in the United States for any employer. At this time, we are not sponsoring or providing assistance with obtaining work authorization.

Nice To Haves

Advertising, media, marketing, attribution, or measurement experience is strongly preferred.
Experience with event-level or impression-level datasets is particularly valuable.
Experience joining datasets with different grains, keys, and identifiers is highly valuable.
Familiarity with advertising attribution or measurement methodologies is a plus.

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.
Analyze relationships between media exposure and downstream outcomes to support measurement and optimization.
Work with datasets at different grains and across different identifiers, including event-level and impression-level data.
Reconcile multiple sources representing similar business events and establish clear approaches when sources disagree.
Partner with analytics, media, technology, and other agency teams to ensure data products answer the right business questions.
Use AI-assisted development tools thoughtfully to explore solutions, debug issues, generate documentation and test cases, and identify potential edge cases.
Review and validate AI-generated outputs rather than treating them as inherently correct.
Document technical decisions, assumptions, dependencies, and data models so others can understand and confidently use what you build.