Associate Director, Data Engineering, Measurement

Attention Arc•Irvington, NY

1d•$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 systems for collecting, connecting, structuring, governing, and activating data that supports measurement, attribution, reporting, optimization, and business decisions. Working across Data Science, Analytics, Media, Product, Engineering, Technology, IT, and Infrastructure, this role translates business and measurement requirements into practical technical solutions and roadmaps. It guides data platform architecture, cloud warehouses and data lakes, integrations, pipelines, models, governance, quality, monitoring, and platform optimization. The Associate Director also provides hands-on technical guidance when needed, mentors engineers, contributes to hiring and development, and helps teams balance immediate needs with long-term reliability, scalability, security, and maintainability. The position is based on a hybrid, in-office model. Employees are expected to work from the Irvington, New York office Tuesday through Thursday, and candidates must reside within commuting distance. The estimated compensation range is $120,000 to $150,000. Candidates must be authorized to work in the United States for any employer; sponsorship is not available.

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 working within 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 evaluate 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; the company does not sponsor or assist with obtaining work authorization.

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 the 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 when needed.
Guide pipelines and integrations supporting media measurement, attribution, conversion tracking, and performance reporting.
Oversee collection, transformation, and organization of measurement data from advertising platforms, analytics tools, customer systems, and other sources.
Support the design and implementation of conversion APIs (CAPI), server-side integrations, and other measurement connectivity solutions.
Partner with Analytics, Data Science, and Media teams to translate attribution requirements into reliable engineering solutions.
Structure and document measurement data for consistent reporting, analysis, and optimization; address discrepancies, incomplete signals, platform limitations, and changing requirements.
Oversee scalable data models for attribution, reporting, analytics, and business intelligence.
Establish standards for data quality, validation, reconciliation, and consistency; guide automated quality checks, monitoring, alerting, and observability.
Ensure pipelines are reliable, resilient, and monitored; develop expectations for incident management, troubleshooting, root-cause analysis, and resolution.
Promote consistent data definitions, documentation, and lineage, and support governance for security, privacy, compliance, and responsible use.
Guide ingestion, transformation, and orchestration processes and integrations involving APIs, event streams, operational databases, advertising platforms, and external data providers.
Evaluate pipeline performance, processing efficiency, storage strategies, and platform costs; identify ways to improve scalability, reduce latency, and optimize cloud utilization.
Establish reusable engineering and integration patterns; collaborate on deployment processes, testing, and production reliability.
Evaluate and implement data technologies, frameworks, and architectural approaches aligned with measurable business and operational needs.
Partner with business and technical leaders to understand current and future requirements, translate objectives into technical recommendations and roadmaps, and prioritize initiatives.
Advise stakeholders on data structure, access, integration, interpretation, technical capabilities, constraints, risks, and architectural tradeoffs.
Align teams around shared data standards, definitions, and quality expectations, and identify improvements to data flows across strategy, activation, optimization, and measurement.
Coach and mentor data engineers and other technical team members; strengthen architectural thinking, engineering judgment, and problem-solving.
Set expectations for code quality, documentation, testing, and technical ownership; participate in architectural reviews and solution design.
Support hiring, onboarding, and professional development, and encourage knowledge sharing, constructive feedback, accountability, curiosity, and continuous improvement.
Stay informed about data engineering, measurement, cloud architecture, and industry practices; evaluate tools and frameworks for meaningful business problems.
Anticipate future data volume, integration complexity, governance, and measurement needs; explore automation and AI-assisted capabilities for engineering workflows and monitoring.
Recommend practical improvements and define success measures for platform initiatives, including quality, reliability, processing performance, cost efficiency, and stakeholder trust.