Associate Director, Data Engineering, Measurement

Attention Arc•Irvington, NY

2d•$120,000 - $150,000•Hybrid

About The Position

The Associate Director, Data Engineering, Measurement is a senior technical leadership role responsible for shaping the architecture, standards, and technical direction of Attention Arc’s data engineering and measurement infrastructure. The role builds and guides reliable, scalable data systems that support measurement, attribution, conversion tracking, reporting, optimization, and business decision-making. It partners across Analytics, Data Science, Media, Product, Engineering, Technology, IT, and Infrastructure to translate business and measurement needs into practical technical solutions. The role oversees data platforms, integrations, pipelines, models, data quality, governance, and platform optimization. It provides architectural guidance and hands-on technical support for complex engineering challenges, while balancing reliability, scalability, performance, cost, security, privacy, and maintainability. The Associate Director also mentors engineers, influences technical decisions, supports hiring and development, and helps teams improve their engineering practices and platform capabilities. The position is hybrid, with employees expected to work in the Irvington, New York office three days per week: Tuesday, Wednesday, and Thursday. Candidates must reside in and be able to commute to that office. Applicants must be authorized to work in the United States; the company does not sponsor work authorization.

Requirements

8–12+ years of professional experience in data engineering, data platform architecture, or a closely related technical discipline, including 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 with data lakes or lakehouse architectures, including technologies such as Amazon S3, Azure Data Lake Storage, Delta Lake, or Apache Iceberg.
Advanced SQL proficiency, 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 with transformation frameworks such as dbt or equivalent technologies.
Experience with orchestration and ingestion tools such as Apache Airflow, Dagster, Fivetran, Stitch, or custom-built 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 in cloud ecosystems; AWS is preferred, with 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 assess architectural tradeoffs involving reliability, scalability, performance, cost, and maintainability.
Experience providing technical direction, reviewing architecture, and mentoring engineers.
Ability to translate complex business and measurement requirements into practical, scalable technical solutions.
Strong communication skills and experience partnering with technical and nontechnical stakeholders.
Bachelor’s degree in Computer Science, Data Engineering, Software Engineering, Information Systems, or a related field, or equivalent practical experience.
Candidates must reside in and be able to commute to the Irvington, New York office three days per week.
Must be authorized to work in the United States for any employer; work authorization sponsorship is not provided.

Nice To Haves

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.

Responsibilities

Help define and guide the long-term architecture, technical direction, and evolution of the agency’s data engineering and measurement infrastructure.
Oversee design and integration of data platforms, including cloud data warehouses, data lakes, ingestion tools, and transformation frameworks.
Establish and maintain standards for data modeling, schema design, pipeline development, access controls, and platform governance.
Guide architectural decisions that balance scalability, reliability, performance, cost, and maintainability; identify opportunities to simplify infrastructure and reduce technical debt.
Partner with Technology and Infrastructure teams on security, privacy, and access management practices.
Provide hands-on technical guidance and contribute to complex engineering challenges as needed.
Guide pipelines and integrations supporting 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 sources.
Support conversion API (CAPI), server-side data integrations, and other measurement connectivity solutions.
Partner with Analytics, Data Science, and Media teams to translate attribution requirements into reliable engineering solutions and address measurement data challenges.
Oversee scalable, documented data models for attribution, reporting, analytics, and business intelligence.
Establish data quality, validation, reconciliation, and consistency standards; guide automated checks, monitoring, alerting, and observability.
Set expectations for pipeline reliability, incident management, troubleshooting, root-cause analysis, and issue resolution.
Promote data definitions, documentation, and lineage; partner with stakeholders on governance, security, privacy, compliance, and responsible data use.
Guide and optimize data ingestion, transformation, orchestration, and integrations involving APIs, event streams, operational databases, advertising platforms, and external data providers.
Evaluate pipeline performance, processing efficiency, storage strategies, platform costs, scalability, latency, and cloud infrastructure utilization.
Establish reusable engineering and integration patterns; collaborate on deployment, testing, and production reliability improvements.
Evaluate and support implementation of data technologies, frameworks, and architectural approaches aligned with business and operational needs.
Partner with cross-functional leaders to understand data requirements and translate business objectives into technical recommendations, platform enhancements, and engineering roadmaps.
Advise stakeholders on data structure, access, integration, interpretation, technical capabilities, constraints, risks, and architectural tradeoffs.
Help prioritize engineering initiatives and align teams around data standards, definitions, quality expectations, and shared goals.
Provide technical guidance, coaching, and mentorship to data engineers and other technical team members.
Establish expectations for code quality, documentation, testing, and technical ownership; participate in architectural reviews and solution design.
Support hiring, onboarding, professional development, knowledge sharing, and continuous improvement within the team.
Stay informed about data engineering, measurement, cloud architecture, and industry practices; evaluate new technologies against business needs.
Anticipate future data volume, integration complexity, governance requirements, and measurement needs; explore automation and AI-assisted capabilities for engineering workflows and monitoring.
Establish success measures for platform initiatives, including data quality, reliability, processing performance, cost efficiency, and stakeholder trust.