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Salary: USD 90,000 - 100,000 / annual
Attention Arc is a 160-person media agency focused on restoring intention to media through deliberate craft and technology. The company operates across four offices (Phoenix, Durham, Irvington, and New York City) and combines strategic media expertise with data-driven solutions for major brands.
As a Data Engineer focused on Advertising Measurement & Attribution, you will architect the data infrastructure and analytical models that translate media exposure into measurable business outcomes. You'll work with 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'll need to understand the analytical questions behind the data, work through ambiguity, identify appropriate assumptions and business rules, and translate complex information into reliable datasets that teams can use to make better decisions.
Key responsibilities include:
- Building and maintaining data models supporting advertising measurement, attribution, reporting, and media optimization
- Transforming, integrating, and reconciling data from multiple platforms and measurement sources
- Developing sophisticated SQL transformations across large analytical datasets
- Building and maintaining dbt models, tests, documentation, dependencies, and data-quality controls
- Using Snowflake to store, transform, analyze, and serve analytical data at scale
- Designing data models that make complex source data understandable, efficient, and actionable for downstream users
- Translating business, media, and measurement requirements into concrete datasets, definitions, and transformations
- Investigating ambiguous data challenges and determining appropriate assumptions, definitions, joins, and business rules
- Diagnosing unexpected data issues and determining whether they originate from source data, business logic, transformations, joins, or model design
- Analyzing relationships between media exposure and downstream outcomes to support measurement and optimization
- Working with datasets at different grains and across different identifiers, including event-level and impression-level data
- Reconciling multiple sources representing similar business events and establishing clear approaches when sources disagree
- Partnering with analytics, media, technology, and other agency teams to ensure data products answer the right business questions
- Using AI-assisted development tools thoughtfully to explore solutions, debug issues, generate documentation and test cases, and identify potential edge cases
- Reviewing and validating AI-generated outputs rather than treating them as inherently correct
- Documenting technical decisions, assumptions, dependencies, and data models so others can understand and confidently use what you build
The data products you create will help teams answer questions such as: Which media exposures are associated with conversions? How should conversions be attributed to media activity? How did a campaign perform across channels, placements, or audiences? What happened after a user or household was exposed to an advertisement? How should data from different measurement systems be reconciled? Which datasets should be considered authoritative when sources disagree? How should high-volume event and exposure data be modeled so analysts can efficiently uncover meaningful insights?
Attention Arc values credibility, clarity, connection, care, and culture & character. The company operates on a hybrid model with three days per week in-office (Tuesday, Wednesday, Thursday) to foster collaboration, mentorship, and hands-on learning.
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
Preferred qualifications:
- Advertising, media, marketing, attribution, or measurement experience
- Experience with event-level or impression-level datasets
- Experience joining datasets with different grains, keys, and identifiers
- Familiarity with advertising attribution or measurement methodologies
Must be authorized to work in the United States. The company is not sponsoring or providing assistance with obtaining work authorization.
