Manager, Data Engineering

Post Consumer Brands•Lakeville, MN

18h•$102,931 - $152,338•Hybrid

About The Position

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.

Requirements

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)

Nice To Haves

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

Responsibilities

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