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About the Role

As part of our Technology & Analytics team, you are one of the Builders and Translators, turning complex data, emerging technologies, and innovative ideas into practical solutions that help our entire agency work smarter. You connect human expertise with the power of technology, building the data foundations that power measurement, attribution, reporting, and more intentional decision-making.

Responsibilities

Help define and guide the long-term architecture, technical direction, and evolution of Attention Arc's data engineering and measurement infrastructure.
Oversee the design and integration of modern data platforms, including cloud data warehouses, data lakes, ingestion tools, and transformation frameworks.
Establish and maintain technical standards for data modeling, schema design, pipeline development, access controls, and platform governance.
Guide architectural decisions that balance scalability, reliability, performance, cost, and maintainability.
Evaluate existing data infrastructure and identify opportunities to simplify architecture, reduce technical debt, and improve operational efficiency.
Ensure data platforms can support evolving business needs, measurement methodologies, and increasing data complexity.
Partner with Technology and Infrastructure teams to maintain appropriate security, privacy, and access management practices.
Provide hands-on technical guidance and contribute directly to complex engineering challenges when needed.
Guide the development and maintenance of data pipelines and integrations that support media measurement, attribution, conversion tracking, and performance reporting.
Oversee the collection, transformation, and organization of measurement data from advertising platforms, analytics tools, customer systems, and other relevant sources.
Support the design and implementation of conversion APIs (CAPI), server-side data integrations, and other measurement connectivity solutions.
Partner with Analytics, Data Science, and Media teams to understand attribution requirements and translate them into reliable data engineering solutions.
Ensure measurement data is structured and documented to support consistent reporting, analysis, and optimization.
Help address challenges related to data discrepancies, incomplete signals, platform limitations, and evolving measurement requirements.
Develop scalable approaches to integrating data from multiple advertising platforms, client systems, and third-party sources.
Support the agency's ability to connect media activity with meaningful business outcomes through accurate, accessible, and well-governed data.
Oversee the design and development of scalable, well-documented data models supporting attribution, reporting, analytics, and business intelligence.
Establish standards for data quality, validation, reconciliation, and consistency across platforms and datasets.
Guide the implementation of automated data quality checks, monitoring, alerting, and observability practices.
Ensure data pipelines are reliable, resilient, and appropriately monitored.
Develop clear expectations for incident management, troubleshooting, root-cause analysis, and issue resolution.
Promote consistent data definitions, documentation, and lineage to strengthen trust and reduce ambiguity.
Partner with Technology and business stakeholders to ensure data governance practices support security, privacy, compliance, and responsible use.
Balance immediate delivery requirements with long-term data integrity, maintainability, and sustainability.
Guide the development and optimization of data ingestion, transformation, and orchestration processes across the agency's data ecosystem.
Oversee integrations involving APIs, event streams, operational databases, advertising platforms, and external data providers.
Evaluate pipeline performance, data processing efficiency, storage strategies, and platform costs.
Identify opportunities to improve scalability, reduce latency, and optimize cloud infrastructure utilization.
Establish reusable engineering patterns and integration approaches that improve consistency across projects.
Collaborate with engineering teams to improve deployment processes, testing practices, and production reliability.
Support the evaluation and implementation of new data technologies, frameworks, and architectural approaches.
Ensure platform improvements are aligned with measurable business and operational needs.
Partner with Data Science, Analytics, Media, Product, Engineering, IT, and Infrastructure leaders to understand current and future data requirements.
Translate business objectives and measurement priorities into technical recommendations, platform enhancements, and engineering roadmaps.
Serve as a trusted technical advisor on how data should be structured, accessed, integrated, and interpreted.
Communicate technical capabilities, constraints, risks, and architectural tradeoffs clearly to both technical and nontechnical stakeholders.
Help prioritize engineering initiatives based on business impact, technical complexity, scalability, and available resources.
Align teams around shared data standards, definitions, and quality expectations.
Identify opportunities to improve how data moves between strategy, activation, optimization, and measurement.
Support initiatives that strengthen the agency's ability to make intentional, data-informed decisions.
Provide technical guidance, coaching, and mentorship to data engineers and other technical team members.
Help strengthen engineering judgment, architectural thinking, and problem-solving capabilities across the team.
Establish clear expectations for code quality, documentation, testing, and technical ownership.
Participate in architectural reviews, technical discussions, and solution design.
Support hiring, onboarding, and professional development of data engineering talent.
Encourage knowledge sharing, constructive feedback, and collaborative problem-solving.
Foster a culture of accountability, curiosity, and continuous improvement.
Lead through expertise, collaboration, and influence, helping others make sound technical decisions.
Stay informed about emerging data engineering technologies, measurement capabilities, cloud architectures, and industry best practices.
Evaluate new platforms, tools, and frameworks based on their ability to solve meaningful business problems.
Anticipate future data volume, integration complexity, governance requirements, and measurement needs.
Identify opportunities to improve data reliability, performance, accessibility, and operational efficiency.
Explore how automation and AI-assisted capabilities can strengthen data engineering workflows and platform monitoring.
Recommend thoughtful architectural improvements that support long-term scalability without introducing unnecessary complexity.
Establish meaningful success measures for platform initiatives, including data quality, reliability, processing performance, cost efficiency, and stakeholder trust.
Help build a data ecosystem where every improvement strengthens the agency's ability to make better decisions.

Requirements

8–12+ years of professional experience in data engineering, data platform architecture, or a closely related technical discipline, including experience providing senior-level technical leadership.
Extensive experience designing, developing, and governing modern cloud-based data platforms and enterprise-scale data pipelines.
Deep expertise with cloud data warehouses such as Snowflake, BigQuery, Amazon Redshift, or Azure Synapse.
Experience working with data lakes or lakehouse architectures, including technologies such as Amazon S3, Azure Data Lake Storage, Delta Lake, or Apache Iceberg.
Advanced proficiency in SQL, including complex transformations, query optimization, data modeling, and performance tuning across large datasets.
Strong experience with Python or comparable programming languages used in data engineering and pipeline development.
Experience using transformation frameworks such as dbt or equivalent technologies.
Experience with orchestration and ingestion tools such as Apache Airflow, Dagster, Fivetran, Stitch, or custom-built data pipelines.
Demonstrated ability to design scalable data models and establish standards for schema design, documentation, and data governance.
Experience integrating data from APIs, event streams, operational databases, and external platforms.
Experience with data quality frameworks, monitoring, observability, alerting, and production incident management.
Strong understanding of data security, privacy, access controls, and compliance requirements.
Experience working within cloud ecosystems, with AWS preferred and Azure or Google Cloud also relevant.
Experience designing or supporting data infrastructure used for measurement, attribution, reporting, or analytics.
Familiarity with conversion APIs (CAPI), server-side integrations, and modern digital measurement data flows.
Demonstrated ability to evaluate architectural tradeoffs involving reliability, scalability, performance, cost, and maintainability.
Experience providing technical direction, reviewing architecture, and mentoring other engineers.
Ability to translate complex business and measurement requirements into practical, scalable technical solutions.
Strong communication skills and experience partnering with both technical and nontechnical stakeholders.
Experience in advertising, media, marketing technology, digital measurement, or another data-intensive environment.
Hands-on experience with Snowflake and AWS-based data infrastructure.
Experience integrating data from major advertising and analytics platforms.
Familiarity with attribution methodologies, conversion tracking, and marketing measurement challenges.
Experience implementing or supporting Meta Conversions API, Google enhanced conversions, or comparable server-side measurement integrations.
Understanding of event-level data, identity considerations, and privacy-conscious measurement practices.
Experience with CI/CD pipelines, infrastructure as code, containerization, and automated testing.
Familiarity with data catalogs, lineage tools, metadata management, and governance frameworks.
Experience supporting data science, advanced analytics, or machine learning workflows.
Experience developing platform roadmaps and contributing to technology investment decisions.
Experience guiding technical teams through platform modernization, architectural changes, or data infrastructure scaling initiatives.
You can evaluate complex technical challenges, make sound architectural recommendations, and guide others toward solutions that balance immediate needs with long-term sustainability.
You understand how data engineering supports business outcomes. You connect technical decisions to measurement needs, operational priorities, and the agency's broader goals.
You recognize how individual data pipelines, models, integrations, and platforms work together. You anticipate downstream impacts and design systems that people can trust.
You approach complex problems with structure, curiosity, and sound judgment. You investigate root causes, evaluate tradeoffs, and develop practical solutions.
You can make complex data systems, technical tradeoffs, and architectural decisions understandable. You establish clear standards, communicate priorities, and help teams understand not only what needs to be built, but why it matters and how it supports business outcomes.
You build strong relationships across disciplines and guide technical direction through expertise, credibility, and partnership.
You enjoy helping others grow, sharing knowledge, providing constructive feedback, and strengthening technical capabilities across the team.
You take responsibility for platform outcomes, proactively address risks, and follow through on commitments.
You stay curious about emerging technologies and evolving measurement requirements while maintaining a thoughtful, practical approach to change.
You bring deep technical expertise, sound architectural judgment, and a strong commitment to data integrity. You establish standards, anticipate risks, and guide decisions that strengthen the reliability, scalability, and trustworthiness of our data ecosystem. Others look to you for thoughtful guidance on complex technical challenges.
You model ownership, curiosity, collaboration, and integrity. You challenge assumptions constructively, communicate difficult realities with honesty and respect, and create an environment where engineers feel supported, accountable, and empowered to do their best work.
You must be authorized to work in the United States for any employer.

Skills

Data Engineering
Data Platform Architecture
Cloud Data Warehouses
Data Lakes
SQL
Python
dbt
Apache Airflow
Dagster
Fivetran
Stitch
Data Modeling
Schema Design
Data Governance
API Integration
Event Stream Integration
Database Integration
Data Quality Frameworks
Monitoring
Observability
Alerting
Incident Management
Data Security
Data Privacy
Access Controls
Compliance
AWS
Azure
Google Cloud
Measurement
Attribution
Reporting
Analytics
Conversion APIs (CAPI)
Server-side Integrations
Architectural Tradeoffs
Technical Leadership
Mentorship
Business Requirement Translation
Communication
Stakeholder Partnership
Advertising Technology
Media Technology
Digital Measurement
Snowflake
Meta Conversions API
Google Enhanced Conversions
CI/CD
Infrastructure as Code
Containerization
Automated Testing
Data Catalogs
Data Lineage
Metadata Management
Data Science Support
Advanced Analytics Support
Machine Learning Support
Platform Roadmapping
Technology Investment Decisions
Platform Modernization
Architectural Changes
Data Infrastructure Scaling

Location

Irvington, New York

Work Type

Hybrid

Experience Level

Associate Director
Senior-level technical leadership
8-12+ years of professional experience

Education Level

Bachelor's degree in Computer Science, Data Engineering, Software Engineering, Information Systems, or a related field, or equivalent practical experience.

Salary/Compensations

$120,000 to $150,000

Benefits

Flexible Paid Time Off
Comprehensive Medical, Dental, and Vision plans
Competitive Parental Leave
Mental health support
Annual agency contribution to 401(k)
Flexible Spending Accounts (FSAs) and Healthcare FSA
Commuter benefits and corporate discounts
Carrot Fertility and family-building support
Employee Assistance Program (EAP)

About the Company

We are a media agency built for brands that value intention. At Attention Arc, we combine deliberate media craft with technology designed to amplify it. We build trusted partnerships so every decision stays aligned with what matters most.
At 160 people across four offices in Phoenix, Durham, Irvington, and New York City, we occupy a deliberate sweet spot: big enough to handle major brands and complex business, nimble enough to actually move, make decisions, and drive impact. No unnecessary layers. No slow-moving bureaucracy. Just sharp thinking, fast execution, and tech-enabled.
Our culture is shaped by our values: All In, No Sidelines, Move With Curiosity, Truth With Heart.

Equal Opportunity

Inclusion isn't just about representation, it's about creating an environment where people feel seen, supported, and comfortable showing up as themselves.
We value different lived experiences and perspectives, and actively create space for those voices to shape our work. We're committed to building an agency where everyone has a genuine seat at the table and is trusted to step in and contribute, fostering collaboration, encouraging curiosity, and creating opportunities for people to take ownership and make an impact.