Sr. Manager, Data & Analytics

Foot Locker•,

3d•$140,000 - $170,000•Remote

About The Position

Foot Locker, Inc. is seeking an experienced Senior Manager Data & Analytics, an innovative individual with a proven track record in building enterprise level Data Platforms to support cross functional Product development and Engineering across multiple lines of business. You must be inspired by technology and have both passion and expertise in creating innovative technical solutions and Data architectures. You will serve as an expert in Data Platforms and enabling technologies, demonstrate strong leadership and champion Foot Locker Inc. values. This role is expected to drive innovation through collaboration across our Data Engineering, Business Intelligence, Data Platforms, Data Architecture, Data science teams and business to help push Foot Locker, Inc. to the next level. The team is embarking on improving data architecture capabilities further to mature and support our business growth. Our current Data & Analytics Platform technology stack comprises of cloud solutions (Microsoft Azure), Databricks, Confluent Kafka, Snowflake and Power BI. Ideal candidate is expected to have a deep implementation experience around Data Lake and Lakehouse architecture patterns, Event driven architectures, ETL, ELT, Dimensional modeling with strong technology experience in platforms such as Snowflake, Databricks and Kafka. This role is fully remote.

Requirements

Bachelor’s degree in computer science or related field
12 years of experience in Data Architecture, Dimensional Modeling and understanding Data Ingestion and transformation design patterns and frameworks
5 years’ experience in a public cloud environment with a focus around Data Architecture and Big Data Technologies
Prior experience working with Event driven Architecture and solutions including ETL, ELT, Master Data Management, Reference Data Management, and Metadata Management
Implementation experience and expert knowledge in Snowflake and Databricks platforms focused on modern data architecture approaches, Data Pipelines & enabling AI, BI and Analytics solutions using Snowflake native capabilities
Demonstrated Influential leadership experience driving architecture solutions for various data and analytics use cases and influencing multiple stakeholders
Analytical and problem-solving skills
Acute attention to detail
Public cloud experience, preferably Azure, AWS, or Google preferred.

Nice To Haves

Prior Retail Industry experience is preferred
Prior experience operating in an Agile Scrum methodology is preferred
Previous experience at a fortune 500 company is preferred

Responsibilities

Lead a global data engineering team, manage all day-to-day data engineering and data operations on the Data platforms globally, ensuring optimum end-user experience and business continuity whilst mitigating risks and meeting delivery milestones.
Mentor, support, and manage team members and ensure they follow well architected principles focused on Operational Excellence, Cost optimization, Metadata Management, Security, Reliability and Performance efficiency.
Design, document, and build reusable components and frameworks at scale to support analytics products.
Design, document and implement product features in collaboration with Products, business and Technology teams
Design and build Canonical Data and Dimensional Modeling at an Enterprise level to support various analytics initiatives such as Descriptive, Diagnostic/Exploratory and Predictive analytics capabilities
Participating, recommending best practices in all phases of DevOps architectural activities, including concept definition, requirements analysis, design, development, integration, installation, operations, maintenance, and decommissioning.
Be a thought leader by helping to define purpose-built Data Platform strategy and lead, influence other solution Architects and Engineers towards building best in class Data and Analytics platforms
Design Data Architecture leveraging Microsoft Azure, Snowflake and Databricks native capabilities to support Batch and Streaming pipelines for Analytics, Data Sharing, Data Science and API use cases