Associate Director, Data Engineering, Measurement

WHAT YOU'LL DO

At Attention Arc, we believe technology should amplify human expertise, not replace it. And when it comes to media measurement, that starts with data people can trust.

As our Associate Director, Data Engineering, Measurement, you'll help shape how data is collected, connected, structured, governed, and activated across the agency. You'll bring the technical expertise and architectural judgment needed to build reliable, scalable data systems that support accurate measurement, meaningful insights, and better business decisions.

You'll work at the intersection of data engineering, measurement technology, and business strategy, partnering with Analytics, Data Science, Media, Product, Engineering, and Technology leaders to ensure our data infrastructure supports how we plan, activate, optimize, and measure media.

This is a senior technical leadership role for someone who thinks beyond individual pipelines or platforms. You'll help define the architecture, standards, and technical direction of our data ecosystem, ensuring information moves reliably between systems and can be confidently used for attribution, reporting, optimization, and decision-making.

You'll guide the development of scalable data models, oversee data integrations, strengthen governance and quality standards, and help the agency navigate the growing complexity of modern measurement. From cloud data warehouses and data pipelines to conversion APIs and evolving attribution requirements, you'll ensure our technology is built around how data is actually used.

You'll also mentor engineers, guide technical decisions, and help teams balance immediate business needs with long-term platform reliability and scalability.

Your impact will be measured by the trust our people place in our data, the strength and scalability of our measurement infrastructure, and the clarity our systems bring to increasingly complex media decisions.
Key Responsibilities

Data Platform Architecture & Technical Leadership

• 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.

Measurement Data Engineering & Attribution Infrastructure

• 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.

Data Modeling, Quality & Governance

• 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.

Data Integration, Performance & Platform Optimization

• 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.

Cross-Functional Strategy & Business Alignment

• 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.

Technical Mentorship & Team Development

• 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.

Innovation & Continuous Improvement

• 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.

WHAT YOU'LL BRING
Experience & Technical Expertise

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

Preferred Qualifications

• 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.

Capabilities

Technical Leadership & Architectural Judgment: You can evaluate complex technical challenges, make sound architectural recommendations, and guide others toward solutions that balance immediate needs with long-term sustainability.

Strategic Thinking & Business Understanding: You understand how data engineering supports business outcomes. You connect technical dec