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Attention Arc

Associate Director, Data Engineering, Measurement

Data EngineeringConfirmed live in the last 24 hours
Full-timeIrvington, NY, USA$120k - $150k

About the Job

Requirements

  • Have 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.
  • Have extensive experience designing, developing, and governing modern cloud-based data platforms and enterprise-scale data pipelines.
  • Have deep expertise with cloud data warehouses such as Snowflake, BigQuery, Amazon Redshift, or Azure Synapse.
  • Have experience working with data lakes or lakehouse architectures, including technologies such as Amazon S3, Azure Data Lake Storage, Delta Lake, or Apache Iceberg.
  • Have advanced proficiency with SQL, including complex transformations, query optimization, data modeling, and performance tuning across large datasets.
  • Have strong experience with Python or comparable programming languages used in data engineering and pipeline development.
  • Have experience using transformation frameworks such as dbt or equivalent technologies.
  • Have experience with orchestration and ingestion tools such as Apache Airflow, Dagster, Fivetran, Stitch, or custom-built data pipelines.
  • Have demonstrated ability to design scalable data models and establish standards for schema design, documentation, and data governance.
  • Have experience integrating data from APIs, event streams, operational databases, and external platforms.
  • Have experience with data quality frameworks, monitoring, observability, alerting, and production incident management.
  • Have a strong understanding of data security, privacy, access controls, and compliance requirements.
  • Have experience working within cloud ecosystems; AWS is preferred, with Azure or Google Cloud also relevant.
  • Have experience designing or supporting data infrastructure used for measurement, attribution, reporting, or analytics.
  • Have familiarity with conversion APIs (CAPI), server-side integrations, and modern digital measurement data flows.
  • Have demonstrated ability to evaluate architectural tradeoffs involving reliability, scalability, performance, cost, and maintainability.
  • Have experience providing technical direction, reviewing architecture, and mentoring other engineers.
  • Be able to translate complex business and measurement requirements into practical, scalable technical solutions.
  • Have a bachelor's degree in Computer Science, Data Engineering, Software Engineering, Information Systems, or a related field, or equivalent practical experience.
  • Have strong communication skills and experience partnering with technical and nontechnical stakeholders.

Responsibilities

  • Help define and guide the long-term architecture, technical direction, and evolution of the agency'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 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.

Desired Qualifications

  • Have experience in advertising, media, marketing technology, digital measurement, or another data-intensive environment.
  • Have hands-on experience with Snowflake and AWS-based data infrastructure.
  • Have experience integrating data from major advertising and analytics platforms.
  • Have familiarity with attribution methodologies, conversion tracking, and marketing measurement challenges.
  • Have experience implementing or supporting Meta Conversions API, Google enhanced conversions, or comparable server-side measurement integrations.
  • Have an understanding of event-level data, identity considerations, and privacy-conscious measurement practices.
  • Have experience with CI/CD pipelines, infrastructure as code, containerization, and automated testing.
  • Have familiarity with data catalogs, lineage tools, metadata management, and governance frameworks.
  • Have experience supporting data science, advanced analytics, or machine learning workflows.
  • Have experience developing platform roadmaps and contributing to technology investment decisions.
  • Have experience guiding technical teams through platform modernization, architectural changes, or data infrastructure scaling initiatives.

About the Company

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