Sr. Manager Data Analytics-1

Job Summary

A role in Merchandising Capabilities means partnering with Merchandising, technology, data science and analytics leaders to co-create solutions that maximizes business value and enables key strategies that are critical to the enterprise and for the experiences we create for our guests. Truly an owner and expert of your product and underlying processes, you will set the vision, align on the roadmap and mobilize teams to deliver needed outcomes. Given most strategies have a business impact, you will also serve as a trusted resource to guide other enterprise partners in their solutions to ensure the success and integrity of end-to-end deliverables.

As a Sr. Manager, Data Analytics in Merchandising, you will be accountable for setting the vision, strategy, roadmap, and quality expectations for data, reporting, analytics, automation, and AI capabilities for a specific business function within our Merchandising organization. You will serve as an advocate of our data-driven culture and a trusted advisor to business leaders, helping identify where analytics and AI can create the greatest business value. In partnership with your team of Data Analysts, you will translate business priorities into a portfolio of scalable solutions, ensure measurable outcomes, and build an effective analytics ecosystem that informs and influences decisions. You will build a high-performing team through talent pipeline planning, hiring, coaching, setting clear expectations, capability development, and performance management. You will partner closely with Merchandising, Product Management, Data Science, Technology, and other analytics teams to align solutions, address capability gaps, and democratize trusted data and insights.

Core responsibilities of this job are described within this job description. Job duties may change at any time due to business needs.

As a Sr. Manager, Data Analytics for Targets Merch Data Analytics team you'll:

Lead an analytics team that supports our world-class Merchandising leadership with data, insights, automation, and AI capabilities that enable faster, smarter, and more scalable decision-making. Collaborate with stakeholders to understand their priorities and roadmaps, translate business problems into a clear portfolio of work, and hold the team accountable for delivering measurable business outcomes.

• Set the vision, strategy, and roadmap for data, reporting, analytics, automation, and AI capabilities for the business area, and prioritize work based on business value, urgency, scalability, and strategic alignment.
• Provide thought leadership in the application of data and analytics, helping business leaders identify opportunities where insights, automation, Generative AI, AI agents, or agentic AI can materially improve decisions and outcomes.
• Translate complex and ambiguous business problems into a portfolio of scalable analytical and AI-enabled solutions, defining clear success measures and ensuring the team remains focused on business outcomes rather than isolated technical deliverables.
• Guide and challenge the teams analytical and solution-design approach, establishing expectations for technical quality, scalability, maintainability, and appropriate use of statistical, analytical, and AI methods.
• Ensure the team has the capability to work effectively with large-scale datasets and modern data ecosystems, including platforms and technologies such as GCP BigQuery, Spark, SQL-based data warehouses, and data pipelines using Airflow or similar orchestration tools.
• Establish standards for evaluating and monitoring AI-driven analytical workflows, including defining appropriate quality metrics such as accuracy and relevance, assessing reliability, and measuring adoption and business impact of AI-enabled solutions.
• Identify and prioritize high-impact Generative AI opportunities and guide the adoption of AI-agent-enabled and agentic AI capabilities to automate repeatable analytical work, improve productivity, accelerate insight generation, and enable faster decision-making.
• Establish and promote responsible and scalable AI practices, including data privacy, governance, bias and risk considerations, explainability, human oversight, and integration with the broader data and analytics ecosystem.
• Ensure analytical methodologies, AI solutions, key assumptions, evaluation approaches, and reusable patterns are appropriately documented to improve transparency, maintainability, knowledge sharing, and reuse across the team.
• Partner closely with Merchandising, Product Management, Data Science, Technology, and analytics partners to align roadmaps, resolve dependencies, identify capability gaps, and democratize access to trusted data and insights.
• Develop trusted relationships with business leaders and communicate complex analytical and technical concepts in clear business terms, ensuring recommendations are actionable and connected to measurable business drivers.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.