Data Architecture & Data Engineering Leader

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Data Architecture & Data Engineering Leader based in the United States.

We are seeking an experienced, hands-on technical leader to shape and oversee the enterprise data architecture and engineering strategy. In this pivotal role, you will lead the development of a scalable, secure and high-performing cloud data platform that supports media operations, analytics, client reporting and innovative data products. You will define the long-term data roadmap, modernize existing infrastructure and establish a reliable, unified data foundation. Working closely with cross-functional leaders across Product, Media, Strategy and Client Services, you will transform complex business needs into valuable, data-driven solutions. You will also build and mentor a growing data team, establish best-in-class engineering practices and drive operational excellence. This is an opportunity to influence the organization's technical direction while enabling the development of new, commercially scalable data offerings.

Accountabilities:
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Define and own the enterprise data architecture and technology roadmap, ensuring alignment with business objectives, governance requirements and long-term growth.

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Design and oversee the end-to-end cloud data platform architecture, covering data ingestion, storage, transformation, modeling, integration and delivery layers.

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Lead the development, optimization and maintenance of scalable batch and streaming data pipelines, leveraging APIs, SFTP, ETL and modern integration technologies.

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Drive the consolidation of fragmented reporting and analytics environments into a unified, trusted source of truth, including the migration of legacy systems to modern cloud platforms such as Google Cloud and BigQuery.

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Establish standardized data models, engineering frameworks, coding conventions, documentation practices, testing procedures and deployment standards.

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Build reusable, product-ready data foundations, curated datasets, shared models and data services that enable the launch and scaling of new commercial data products.

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Collaborate with Product Owners and business stakeholders to translate new product concepts into technical requirements, assess data feasibility and identify opportunities to develop differentiated data assets.

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Design reliable data APIs and multi-tenant delivery architectures that support client-facing applications, dashboards and portals while ensuring appropriate access controls and data separation.

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Drive automation across ingestion, transformation, validation, reporting and client data delivery processes to improve efficiency, accuracy and scalability.

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Leverage AI-powered tools and agents to accelerate engineering workflows, automate source mapping, improve pipeline development and testing, detect anomalies and troubleshoot data issues.

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Establish robust data quality, monitoring and observability frameworks, implementing automated alerts, validation processes, service levels and incident management procedures.

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Ensure data architecture incorporates security, privacy and regulatory compliance by design, particularly when handling sensitive and regulated information.

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Define and enforce enterprise data governance standards covering data ownership, classification, access management, lineage, cataloging, retention and auditability.

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Assess third-party data providers, technology vendors and licensing arrangements, making informed build-versus-buy recommendations while managing platform costs and risks.

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Recruit, lead, develop and mentor a growing team of data engineers and analysts, including distributed team members across international locations.

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Introduce modern engineering and delivery practices, including version control, CI/CD, code reviews, documentation, sprint planning and structured release cycles.

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Provide technical leadership to internal contributors and external partners while communicating complex technical concepts and strategic recommendations to executive and non-technical stakeholders.

Requirements:

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16+ years of professional experience in data engineering or data architecture, including at least 5 years in technical leadership or team management roles.

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Demonstrated experience designing, implementing and operating enterprise-scale cloud data platforms, preferably within the Google Cloud ecosystem and BigQuery.

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Proven ability to build data architectures that support commercial data products, client-facing platforms and advanced analytics, beyond traditional internal reporting environments.

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Strong hands-on proficiency in SQL and Python, with extensive knowledge of data modeling approaches, including dimensional, layered and medallion architectures.

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Experience with data pipeline orchestration and transformation technologies such as Airflow, Cloud Composer and dbt.

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Solid understanding of cloud data warehouses, including BigQuery, Snowflake and Amazon Redshift, as well as modern data integration and ETL solutions.

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Proven track record leading complex data migrations, platform modernization initiatives and the consolidation of multiple data sources into unified architectures.

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Experience implementing data quality frameworks, observability solutions, automated testing and monitoring tools.

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Strong knowledge of data governance, security, privacy and regulatory compliance, with practical experience managing sensitive or regulated data under frameworks such as HIPAA, GDPR, CCPA and SOC 2.

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Ability to establish technical standards, processes, documentation and governance structures in organizations at an early stage of technical maturity.

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Strong strategic thinking, problem-solving and decision-making skills, with the ability to balance architectural vision and hands-on technical execution.

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Excellent communication and stakeholder management skills, including the ability to translate complex technical concepts into actionable business recommendations.

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Demonstrated ability to build high-performing teams, mentor technical talent and coordinate delivery across different time zones.

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Experience in media, advertising, marketing technology or healthcare and pharmaceutical marketing is highly desirable.

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Familiarity with healthcare professional (HCP) data, NPI matching, media and campaign data, audience intelligence and marketing analytics is a plus.

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Knowledge of data product management, data contracts, versioning, multi-tenant architectures and configurable data delivery models is advantageous.

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Experience managing third-party data licensing, vendor relationships and data partnership models is beneficial.

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Hands-on familiarity with AI-assisted development tools and agents, such as GitHub Copilot, Codex or Gemini in BigQuery, is preferred.

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Experience with business intelligence platforms such as Tableau, Power BI or Looker, and integration tools such as Fivetran or Airbyte, is an advantage.

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Familiarity with data cataloging and lineage solutions such as Dataplex, Collibra or Atlan is desirable.

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Google Cloud Professional Data Engineer or Cloud Architect certification is a plus.

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Experience managing distributed or offshore engineering teams, particularly across European locations, is preferred.

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Must be based in the United States and comfortable working remotely. Candidates located in the Eastern Time Zone, or willing to work EST hours, are strongly preferred.

Benefits:

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Fully remote position within the United States.

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Opportunity to shape enterprise-wide data architecture and influence the organization's long-term technology strategy.

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High-impact leadership role with ownership of major data engineering, platform modernization and product development initiatives.

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Opportunity to build, lead and mentor a growing international data engineering team.

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Exposure to innovative data products, advanced analytics, AI-assisted engineering and modern cloud technologies.

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Collaborative environment with direct engagement across Product, Media, Strategy, Client Services and executive leadership.

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Opportunity to establish technical standards, introduce modern engineering practices and drive meaningful organizational transformation.