Director, Data Product Execution

Assurant

1d•$121,700 - $200,600•Remote

About The Position

The Director, Data Product Execution, will play a pivotal role in building and guiding high-performing analytics engineering teams within our Data Analytics Product organization. This role will bridge the gap between data engineering, analytics, and product domains, enabling data-driven decision-making through scalable, reliable, and well-governed data products. This Leader will collaborate with product managers, data scientists, and business stakeholders to define and deliver domain-aligned data solutions, ensuring that each product team has the technical guidance they need to deliver high quality solutions.

Requirements

Bachelor’s in Data Engineering, Computer Science, Information Systems, or a related field. Master’s degree is desired.
8+ years of experience in data engineering, analytics engineering, or BI architecture
3+ years in a leadership role.
Proven experience working in or transitioning to a Product Operating Model, with deep understanding of domain-driven design and agile delivery.
Proven experience in driving innovation & improving speed of delivery.
Expertise with SQL, Python, Microsoft Fabric, Databricks, or similar cloud data platforms.
Strong understanding of data modeling and modern data stack principles.
Familiarity with BI tools (Power BI, Tableau, etc.) and how to design models to support them.
Experience implementing CI/CD for data pipelines and data observability solutions.
Leadership & Soft Skills Excellent communication and stakeholder management skills across technical and non-technical teams.
Proven ability to translate business problems into data solutions and measure their impact.
Strong strategic mindset and ability to balance innovation with delivery.

Nice To Haves

Master’s degree is desired.

Responsibilities

Strategic Leadership & Product Alignment Guide the analytics engineering teams in alignment with the department’s Operating Model, embedding technical data capabilities within cross-functional product teams.
Partner with product and other data analytics leaders to define technical data product strategies, ensuring alignment with business goals and KPIs.
Champion data-as-a-product principles, ensuring reusability, quality, and discoverability across the enterprise.
Technical Leadership Consult on and oversee the design of modern data pipelines, semantic layers, and analytical data models supporting product and business analytics needs.
Drive data quality, lineage, observability, and governance practices across analytics product engineering workflows.
Enable the development of self-service analytics through well-structured, reliable datasets and documentation.
Guide engineering team members through the various prototyping methodologies for testing feasibility and usability.
Provide guidance and recommendations for automated testing solutions Provide recommendations on moving toward a continuous improvement/continuous delivery model within the analytics product organization.
Team Development & Collaboration Guide and mentor teams of data engineers & data scientists, fostering a culture of technical excellence, collaboration, and continuous improvement.
Partner with data scientists, analysts, and engineers to promote best practices in data modeling, version control, and CI/CD for analytics workflows.
Work closely with product managers to prioritize data initiatives that deliver measurable value.
Operational Excellence Own and improve data SLAs, performance metrics, and operational dashboards to ensure reliability and scalability.
Collaborate with governance and compliance teams to enforce data privacy, security, and ethical usage standards.
Continuously evaluate and implement emerging technologies and methodologies to enhance data productivity.
Collaboration and Communication Collaborate with cross-functional teams, including IT, marketing, sales, and customer service, to align data strategies and initiatives. This can also include collaborating with Data Stewards in other departments.
Communicate data governance policies and procedures to stakeholders and provide training on data management best practices.
Facilitate data-related discussions and workshops, fostering a culture of data stewardship and accountability.