Data Engineering Manager

• Develop technical roadmaps aligned with business priorities and long-term organizational objectives.
• Lead the technical design and delivery of complex initiatives across multiple systems and platforms.
• Recommend improvements to architecture, scalability, reliability, security, performance, and operational processes.
• Evaluate emerging technologies and industry best practices to enhance platform capabilities.
• Guide engineering teams on architectural decisions, code quality, design reviews, and technical standards.
• Assist in diagnosing and resolving highly complex technical and operational issues.

• Build foundational data capabilities that support: Product recommendation engines Customer 360 platforms Customer identity resolution Clickstream analytics Purchase behavior analysis Real-time personalization Search relevance optimization Behavioral event processing
• Product recommendation engines
• Customer 360 platforms
• Customer identity resolution
• Clickstream analytics
• Purchase behavior analysis
• Real-time personalization
• Search relevance optimization
• Behavioral event processing
• Partner with Data Scientists and Machine Learning Engineers to operationalize and scale personalization models.
• Enable experimentation, A/B testing, feature engineering, and measurement frameworks that improve customer experiences.

• Collaborate closely with Product Management, Data Science, Machine Learning Engineering, Search Engineering, Customer Experience teams, and business stakeholders.
• Translate business objectives into scalable technical solutions and execution plans.
• Communicate technical strategy, progress, risks, recommendations, and outcomes to leaders and stakeholders.
• Lead cross-functional initiatives with significant business impact and organizational visibility.

• Establish operational objectives, work plans, staffing strategies, and resource allocations.
• Ensure adherence to budgets, timelines, and performance requirements.
• Implement strategic policies, processes, and standards that support departmental and organizational objectives.
• Drive continuous improvement through modern engineering practices, automation, observability, and operational excellence.

• 8+ years of experience in software engineering, data engineering, or related technical disciplines.
• 3+ years of experience leading and developing engineering teams.
• Proven experience delivering large-scale data platform, analytics, or machine learning infrastructure initiatives.
• Experience managing technical roadmaps, cross-functional projects, and engineering delivery.

• Strong leadership skills with demonstrated success building and managing high-performing engineering teams.
• Expert knowledge of data architecture, distributed systems, software design patterns, and engineering best practices.
• Deep understanding of data modeling, ETL/ELT, streaming architectures, and event-driven systems.
• Strong expertise with: Python SQL Apache Spark Kafka Data orchestration frameworks
• Python
• SQL
• Apache Spark
• Kafka
• Data orchestration frameworks
• Experience with cloud platforms such as AWS and/or Google Cloud Platform.
• Experience with modern data lake and lakehouse architectures.
• Experience building APIs, data products, and services supporting machine learning applications.
• Strong understanding of scalability, reliability, security, observability, and performance engineering.
• Ability to lead technical strategy while balancing business priorities and organizational goals.
• Strong communication and stakeholder management skills.

• Experience in retail, e-commerce, digital commerce, or customer-facing digital products.
• Experience supporting: Personalized product search Search ranking and relevance systems Recommendation engines Customer personalization platforms Customer 360 initiatives
• Personalized product search
• Search ranking and relevance systems
• Recommendation engines
• Customer personalization platforms
• Customer 360 initiatives
• Experience working with clickstream, behavioral, transactional, and customer identity data.
• Familiarity with: Recommendation systems Collaborative filtering Embeddings and feature engineering Vector search and semantic search technologies MLOps platforms Feature stores Experimentation frameworks and A/B testing
• Recommendation systems
• Collaborative filtering
• Embeddings and feature engineering
• Vector search and semantic search technologies
• MLOps platforms
• Feature stores
• Experimentation frameworks and A/B testing
• Experience supporting machine learning platforms and production AI/ML workloads.

• Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related field, or equivalent combination of education and professional experience.

• Ability to function in a fast-paced, multi-priority environment.
• Ability to travel as needed.
• May require occasional extended hours to support critical business initiatives and production events.

The responsibilities and qualifications outlined above describe the general nature and level of work assigned to this position and are not intended to be an exhaustive list of all duties, responsibilities, or skills required.

may be modified at any time based on business needs.

Last revised: 11/01/2024