Location Description

Post Consumer Brands corporate headquarters in Lakeville, Minnesota, is about 20 miles south of Minneapolis and St. Paul, Lakeville has all the benefits of smaller town living with access to everything a large metropolitan area has to offer. Join more than 400 team members collaborating on the two-building campus to help put breakfast on the tables of millions of consumers in North America.

Responsibilities

Manager, Data Engineering

Location: Hybrid in Lakeville, MN

About the Role

At Post Consumer Brands, data is a strategic asset and the Manager, Data Engineering plays a pivotal role in shaping how we turn data into insight, impact, and innovation. This role leads the design, development, and operation of our modern data engineering foundation, powering trusted analytics, self-service reporting, and AI-ready data products across the enterprise.

You'll lead a team of data engineers responsible for building high-quality, scalable data pipelines, data models, and semantic layers that support critical business decisions. Acting as both a people leader and technical leader, you'll balance delivery speed, reliability, and forward-looking modernization, while partnering closely with Analytics Solutions, Architecture, Governance, and business stakeholders.

This role reports to the Associate Director of Data & Analytics and serves as a key technical leader within our evolving, product-oriented data ecosystem.

Key Responsibilities

Data Engineering Foundation & Delivery

• Lead the design, development, and maintenance of data pipelines, data models, semantic models, and data platforms aligned to enterprise standards
• Deliver curated, durable, and reusable data assets aligned to priority business decision domains
• Ensure timely, accurate, and reliable availability of data across enterprise analytics use cases

Technical Design & Scalability

• Partner with Data & Analytics Architecture to align solutions with enterprise standards and best practices
• Ensure platforms and pipelines are scalable, performant, maintainable, and adaptable
• Make informed tradeoff decisions across performance, cost, and delivery speed

Modern Data Ecosystem Enablement

• Build AI/ML-ready and product-oriented data engineering capabilities
• Engineer low-latency and real-time data pipelines for operational analytics and intelligent applications
• Develop ingestion and processing frameworks for structured and unstructured data

Data Trust, Reliability & Operational Readiness

• Embed data quality, documentation, lineage, and observability into data assets
• Ensure production-grade reliability and operational readiness in partnership with Governance and D&A Operations

Enablement of Analytics & Self-Service

• Partner with Analytics Solutions to enable BI, advanced analytics, and data science
• Deliver trusted, well-modeled semantic layers that empower self-service analytics

Team Leadership & Capability Development

• Lead, mentor, and develop a team of on-shore data engineers and coordinate with off-shore partners
• Foster a culture of engineering excellence, accountability, and continuous improvement
• Drive adoption of modern tools, practices, and AI-enabled engineering workflows

Operational Excellence & Continuous Improvement

• Translate enterprise priorities into executable delivery plans
• Balance speed, quality, and sustainability across delivery and operations
• Leverage automation and AI tools to improve efficiency and code quality

Qualifications

What We're Looking For

Required Qualifications

• 5+ years of experience in data engineering, data warehousing, or related disciplines
• 2+ years of experience leading data engineering teams and enterprise-scale platforms
• Strong experience with modern cloud data stacks (e.g., Snowflake, dbt, ELT tools)
• Proven expertise in data modeling, including dimensional and domain-oriented/semantic models
• Experience building production-grade pipelines with testing, monitoring, and CI/CD
• Familiarity with data quality, observability, and governance practices
• Experience leveraging AI tools to accelerate engineering workflows
• Strong collaboration, communication, and stakeholder-management skills
• Bachelor's Degree REQUIRED in a technical or quantitative field (advanced degree preferred)

Preferred Qualifications

• Experience in CPG, manufacturing, supply chain, or commercial analytics
• Experience in federated or product-oriented data operating models
• Exposure to real-time streaming, generative AI, LLM-enabled analytics, or RAG architectures
• Working knowledge of metadata, lineage, access controls, and data governance concepts
• Strong business acumen and comfort operating in ambiguity

Why You'll Love This Role

At Post Consumer Brands, you'll find big company opportunity with a small company attitude, the chance to work with iconic brands and enterprise-scale data, while still having the autonomy to make meaningful impact. You'll help shape a modern, AI-ready data foundation, lead and grow a talented engineering team, and directly influence how data drives smarter decisions across the business.

We value curiosity, ownership, and outcomes. You'll be trusted to build clarity, elevate standards, and move the organization forward, while being supported by a collaborative, growth-oriented culture that respects work-life balance and personal development.

Ready to Make an Impact?

If you're excited to lead, build, and modernize data engineering capabilities that power real business outcomes and you thrive in an environment where your ideas and leadership truly matter, we'd love to hear from you! Apply today and help shape the future of data at Post Consumer Brands.
The pay range for this position is $102,931 - $152,338 per year.