Director, Technology

• Set and execute a multi-year data and technology strategy aligned to Marketing Automation, MarTech, personalization, customer engagement, and Digital Operations priorities.
• Lead three interconnected capabilities: data engineering and platform ownership; software engineering and integration services; and AI, innovation, experimentation, and emerging technology.
• Define architecture standards and make pragmatic build, buy, partner, and platform decisions that balance speed, scale, reliability, security, and cost.
• Build and evolve scalable data pipelines, data products, APIs, integrations, services, workflow automation, and developer tooling across the MarTech ecosystem.
• Establish strong practices for data quality, observability, lineage, access, governance, privacy, security, service ownership, and incident response.
• Partner with Analytics and business stakeholders to create trusted metrics, semantic models, and self-service capabilities that support better decisions.
• Enable advanced analytics, experimentation, machine learning, generative AI, and intelligent automation through fit-for-purpose data and engineering foundations.
• Establish the foundations for AI adoption, including model and prompt evaluation, data readiness, monitoring, cost management, access controls, human oversight, and responsible AI guardrails.
• Lead an innovation portfolio from problem discovery and prototyping through evaluation, productionization, or intentional shutdown - ensuring successful experiments become supported and measurable products.
• Create alignment across stakeholders on roadmaps, priorities, dependencies, investment choices, risks, and trade-offs, and communicate progress clearly to senior leadership.
• Build, lead, and develop a high-performing organization of engineering leaders and individual contributors across data, software, and innovation.
• Establish an operating model that balances committed delivery, platform health, technical debt reduction, modernization, and responsible experimentation.
• Improve efficiency through automation, reusable platforms, technology simplification, cloud and vendor cost management, and disciplined portfolio planning.

• 10+ years of experience in data engineering, software engineering, platform engineering, or a related discipline, including 3+ years leading managers or multi-disciplinary engineering teams.
• Experience defining and executing technology strategy across complex, enterprise-scale environments.
• A track record of building and operating production-grade data platforms, pipelines, integrations, software services, and automation.
• Strong knowledge of data architecture, data modeling, ETL/ELT, batch and streaming systems, APIs, event-driven design, orchestration, and distributed processing.
• Experience with SQL, Python, cloud platforms such as AWS, GCP, or Azure, and technologies such as Spark, CI/CD, observability, and infrastructure automation.
• Experience integrating CRM, marketing automation, customer data, analytics, experimentation, and personalization platforms. Experience with Salesforce Marketing Cloud, Adobe Marketo, Eloqua, or comparable platforms is a plus.
• Experience establishing data quality, governance, security, privacy, reliability, responsible AI, and operational excellence practices.
• Experience leading software delivery, production operations, incident management, modernization, and platform simplification.
• Experience applying machine learning, generative AI, intelligent automation, or AI-enabled engineering practices in production or enterprise environments.
• Strong communication and influencing skills, with the ability to translate complex technical concepts and trade-offs for technical and non-technical audiences.
• A track record of building high-performing teams, developing leaders, and creating effective partnerships across functions.
• Sound judgment in balancing innovation and speed with sustainability, control, maintainability, and long-term platform health.
• Preferred qualifications
• Experience with customer data platforms, identity resolution, consent management, audience activation, or personalization ecosystems.
• Experience with machine learning, generative AI, predictive analytics, AI agents, model operations, LLM operations, experimentation platforms, or AI-enabled engineering productivity.
• Familiarity with responsible AI practices, including evaluation, explainability, safety, human oversight, and lifecycle governance.
• Experience designing data products, semantic layers, metrics platforms, or self-service analytics capabilities.
• Experience leading cloud migration, platform consolidation, technology simplification, or other multi-year modernization programs.
• Familiarity with GDPR, CCPA, or comparable privacy and data protection requirements.
• Experience managing strategic technology vendors, external partners, and enterprise platform dependencies.
• Experience working in a global, matrixed, consumer-facing, or high-volume digital environment.

The total cash range for this position in Austin is $231,000.00 to $323,500.00. Employees in this role have the potential to increase their pay up to $369,500.00, which is the top of the range, based on ongoing, demonstrated, and sustained performance in the role.

Starting pay for this role will vary based on multiple factors, including location, available budget, and an individual's knowledge, skills, and experience. Pay ranges may be modified in the future.