Data Engineering Manager - Advanced Analytics

At Niagara, we’re looking for Team Members who want to be part of achieving our mission to provide our customers the highest quality most affordable bottled water.

Consider applying here, if you want to:   

• Work in an entrepreneurial and dynamic environment with a chance to make an impact.   
• Develop lasting relationships with great people.   
• Have the opportunity to build a satisfying career.

We offer competitive compensation and benefits packages for our Team Members.

Data Engineering Manager - Advanced Analytics

As a key leader within our data and analytics function, the Advanced Analytics Manager plays a critical role in architecting and maintaining the data infrastructure that underpins enterprise analytics. This role leads a team of data engineers and analytics professionals, focusing on the design, implementation, and optimization of scalable, reliable, and secure data pipelines, especially for complex, high-volume sources such as IoT and sensor-based systems. Working cross-functionally with operations, IT, and business stakeholders, the Advanced Analytics Manager ensures that data from diverse sources, including real-time telemetry, manufacturing systems, and traditional enterprise platforms are efficiently ingested, transformed, and made accessible for analytical consumption. In addition, the role includes light but growing exposure to Generative AI use cases, such as document summarization, chat interfaces for data access, and large language model (LLM) integration—especially in scenarios that augment data accessibility and user experience across the organization. This position requires not only technical expertise but also strategic thinking and leadership skills to evolve data infrastructure, support analytics scalability, and drive a data-first culture. A proven ability to lead high-performing teams and deliver impactful solutions in dynamic, data-rich environments is essential for success.

• Lead and manage a team of data engineers and analytics professionals, providing strategic direction, mentorship, and hands-on support to foster a collaborative, high-performing team environment focused on delivering impactful data solutions.
• Establish clear objectives and key results (OKRs) for the team that align with the enterprise analytics and data strategy, ensuring close coordination with business goals and evolving priorities.
• Conduct regular performance reviews, deliver constructive feedback, and champion continuous learning by identifying training, upskilling, and development opportunities for team members.
• Partner with cross-functional teams, including business analysts, data scientists, IT, and key business stakeholders, to understand data requirements and deliver solutions that support business intelligence, operational reporting, and advanced analytics use cases.
• Define and implement best practices in data engineering, covering data modeling, pipeline orchestration (ETL/ELT), data integration, and data quality, while ensuring reliable access to data from diverse sources such as databases, APIs, cloud platforms, and IoT systems.
• Drive continuous improvement and innovation in data architecture and engineering techniques, with a focus on increasing scalability, performance, and reusability across the analytics ecosystem.
• Oversee project planning and execution, balancing team capacity with priority management, delivery timelines, and quality standards to ensure successful outcomes for strategic and operational analytics initiatives.
• Collaborate with cross-functional business and IT stakeholders to define and implement data and analytics strategies that support enterprise decision-making, ensuring data integrity, security, and governance across all analytical initiatives.
• Provide technical leadership in data engineering and analytics infrastructure, leveraging modern cloud-native tools to enable high-performance data platforms that support BI, reporting, and advanced analytics.
• Champion a culture of data innovation and continuous learning, encouraging the team to explore new data patterns, tooling, and architectural practices that improve scalability, reliability, and time-to-insight.
• Oversee resource planning, vendor coordination, and budget management for the analytics engineering function, ensuring alignment with strategic priorities and operational efficiency.
• Lead the design and implementation of high-volume, scalable data pipelines, models, and data marts across Data Lakes and Data Warehouses to support reporting, dashboarding, and analytics workloads.
• Translate technical and project outcomes into business context through executive-rea