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:
• Define and own the enterprise data architecture and technology roadmap, ensuring alignment with business objectives, governance requirements and long-term growth.
• Design and oversee the end-to-end cloud data platform architecture, covering data ingestion, storage, transformation, modeling, integration and delivery layers.
• Lead the development, optimization and maintenance of scalable batch and streaming data pipelines, leveraging APIs, SFTP, ETL and modern integration technologies.
• 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.
• Establish standardized data models, engineering frameworks, coding conventions, documentation practices, testing procedures and deployment standards.
• Build reusable, product-ready data foundations, curated datasets, shared models and data services that enable the launch and scaling of new commercial data products.
• 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.
• 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.
• Drive automation across ingestion, transformation, validation, reporting and client data delivery processes to improve efficiency, accuracy and scalability.
• Leverage AI-powered tools and agents to accelerate engineering workflows, automate source mapping, improve pipeline development and testing, detect anomalies and troubleshoot data issues.
• Establish robust data quality, monitoring and observability frameworks, implementing automated alerts, validation processes, service levels and incident management procedures.
• Ensure data architecture incorporates security, privacy and regulatory compliance by design, particularly when handling sensitive and regulated information.
• Define and enforce enterprise data governance standards covering data ownership, classification, access management, lineage, cataloging, retention and auditability.
• Assess third-party data providers, technology vendors and licensing arrangements, making informed build-versus-buy recommendations while managing platform costs and risks.
• Recruit, lead, develop and mentor a growing team of data engineers and analysts, including distributed team members across international locations.
• Introduce modern engineering and delivery practices, including version control, CI/CD, code reviews, documentation, sprint planning and structured release cycles.
• Provide technical leadership to internal contributors and external partners while communicating complex technical concepts and strategic recommendations to executive and non-technical stakeholders.
Requirements:
• 16+ years of professional experience in data engineering or data architecture, including at least 5 years in technical leadership or team management roles.
• Demonstrated experience designing, implementing and operating enterprise-scale cloud data platforms, preferably within the Google Cloud ecosystem and BigQuery.
• Proven ability to build data architectures that support commercial data products, client-facing platforms and advanced analytics, beyond traditional internal reporting environments.
• Strong hands-on proficiency in SQL and Python, with extensive knowledge of data modeling approaches, including dimensional, layered and medallion architectures.
• Experience with data pipeline orchestration and transformation technologies such as Airflow, Cloud Composer and dbt.
• Solid understanding of cloud data warehouses, including BigQuery, Snowflake and Amazon Redshift, as well as modern data integration and ETL solutions.
• Proven track record leading complex data migrations, platform modernization initiatives and the consolidation of multiple data sources into unified architectures.
• Experience implementing data quality frameworks, observability solutions, automated testing and monitoring tools.
• 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.
• Ability to establish technical standards, processes, documentation and governance structures in organizations at an early stage of technical maturity.
• Strong strategic thinking, problem-solving and decision-making skills, with the ability to balance architectural vision and hands-on technical execution.
• Excellent communication and stakeholder management skills, including the ability to translate complex technical concepts into actionable business recommendations.
• Demonstrated ability to build high-performing teams, mentor technical talent and coordinate delivery across different time zones.
• Experience in media, advertising, marketing technology or healthcare and pharmaceutical marketing is highly desirable.
• Familiarity with healthcare professional (HCP) data, NPI matching, media and campaign data, audience intelligence and marketing analytics is a plus.
• Knowledge of data product management, data contracts, versioning, multi-tenant architectures and configurable data delivery models is advantageous.
• Experience managing third-party data licensing, vendor relationships and data partnership models is beneficial.
• Hands-on familiarity with AI-assisted development tools and agents, such as GitHub Copilot, Codex or Gemini in BigQuery, is preferred.
• Experience with business intelligence platforms such as Tableau, Power BI or Looker, and integration tools such as Fivetran or Airbyte, is an advantage.
• Familiarity with data cataloging and lineage solutions such as Dataplex, Collibra or Atlan is desirable.
• Google Cloud Professional Data Engineer or Cloud Architect certification is a plus.
• Experience managing distributed or offshore engineering teams, particularly across European locations, is preferred.
• 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:
• Fully remote position within the United States.
• Opportunity to shape enterprise-wide data architecture and influence the organization's long-term technology strategy.
• High-impact leadership role with ownership of major data engineering, platform modernization and product development initiatives.
• Opportunity to build, lead and mentor a growing international data engineering team.
• Exposure to innovative data products, advanced analytics, AI-assisted engineering and modern cloud technologies.
• Collaborative environment with direct engagement across Product, Media, Strategy, Client Services and executive leadership.
• Opportunity to establish technical standards, introduce modern engineering practices and drive meaningful organizational transformation.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
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