Senior Director, Data Platform & Lakehouse Strategy

Dynata; United States; On-site; USD 150,000 - 200,000; Full time

Job summary

Dynata is seeking a Sr. Director of Data Platform Products to lead the strategy, development, and adoption of its next-generation data platform and lakehouse ecosystem.

This leader will define the product vision and ensure governance and scalable capabilities across the organization. Reporting to the SVP, AI & Data Science, the role will set the roadmap for a reusable platform, drive investments, and build a team to scale data products, semantic layers, and AI-ready datasets across analytics and

Qualifications

• 10+ years of experience in product management, platform products, data platforms, data infrastructure, analytics, or related fields.
• Proven experience leading large-scale data platform, lakehouse, data warehouse, data mesh, or data infrastructure initiatives.
• Strong understanding of modern data architectures and technologies, including lakehouse, metadata, governance, semantic layers, ontologies, feature stores, APIs, and platforms such as Snowflake, the Apache ecosystem, Iceberg, Spark, Kafka, and Airflow.
• Experience translating complex business requirements into scalable platform capabilities that support analytics, optimization, decision support, and data product use cases.
• Demonstrated success defining product strategy and roadmaps for enterprise platforms or technical products.
• Demonstrated success leveraging consultants and contractors in addition to internal resources.
• Strong analytical and business acumen, with the ability to evaluate platform investments and prioritize competing needs.
• Strong communication and stakeholder management skills, including experience influencing VP-level and executive stakeholders.
• Demonstrated success operating effectively in ambiguous environments and leading early-stage platform initiatives, balancing strategic leadership with hands-on execution.
• Experience building and leading high-performing product teams.
• Experience working with cloud platforms, preferably AWS, data engineering, analytics, AI/ML, or enterprise software ecosystems preferred.

Responsibilities

• Define and execute the vision, strategy, and roadmap for Dynata's enterprise data platform and lakehouse ecosystem.
• Lead platform delivery and adoption, partnering with engineering, architecture, data science, and vendor teams.
• Translate business and product requirements into platform capabilities that support current and future use cases.
• Partner with business stakeholders to ensure platform-enabled capabilities are adopted, trusted, and embedded into operational and commercial decision-making.
• Continuously evaluate platform performance and identify opportunities for optimization, expansion, and innovation.
• Define and prioritize business use cases enabled by the platform, including feasibility prediction, dynamic pricing, router optimization, graph analytics, syndicated data products, AI-ready datasets, and advanced analytics.
• Ensure platform capabilities are designed to support reusable, scalable, and governed data products.
• Balance near-term business needs with long-term platform strategy and architectural sustainability.
• Partner with business stakeholders to continuously identify and validate new platform-enabled opportunities.
• Cross-functional leadership with Product, Panel, Technology, Research & Data Science, and Commercial leaders.

Skills

Product management

Data platforms

Lakehouse

Governance

Stakeholder management

AWS

APIs

Team leadership

Analytics

Tools

Snowflake

Apache ecosystem

Iceberg

Spark

Kafka

Airflow

Job description

Dynata is seeking a Sr. Director of Data Platform Products to lead the strategy, development, and adoption of its next-generation data platform and lakehouse ecosystem.

This leader will define the product vision and ensure governance and scalable capabilities across the organization. Reporting to the SVP, AI & Data Science, the role will set the roadmap for a reusable platform, drive investments, and build a team to scale data products, semantic layers, and AI-ready datasets across analytics and