Manager Data and Analytics Engineering- (GCP​/Snowflake

Position: Manager Data and Analytics Engineering- (GCP/Snowflake)
The Manager, Data and Analytics Engineering leads a team of engineers responsible for delivering scalable, secure, and high-performing data platforms, pipelines, and analytics solutions across business domains. This role drives end-to-end execution of data initiatives, ensuring high standards of engineering excellence, governance, and delivery discipline.

As a Team Member leader, the Manager is accountable for building and developing technical talent, fostering a culture of innovation and ownership, and aligning team efforts with enterprise priorities. The ideal candidate combines strong engineering experience with business acumen, stakeholder partnership, and a continuous improvement mindset to accelerate the impact of data and analytics across the organization. This role serves as a key bridge between engineering execution and strategic delivery, guiding the team in building well-governed, high-impact data assets that support cross-functional analytics, decision automation, and AI readiness.

This position is located in Springfield, MO. Remote work is not an option for this role.

Responsibilities and Duties:

* Provide hands-on leadership in the design, development, and deployment of enterprise-grade data platforms, batch and streaming pipelines, semantics layer and analytics-enabling services.

* Ensure the team follows best practices in data engineering, architecture patterns (e.g., medallion, data mesh), and platform-specific optimization (e.g., Snowflake, Big Query, dbt, Airflow, Prefect).

* Guide implementation of secure, cost-efficient, and reusable data products, frameworks, and interfaces across ingestion, transformation, semantics and delivery layers.

* Promote adherence to CI/CD, observability, schema management, and infrastructure-as-code practices for resilient data product deployment.

* Own the successful delivery of data initiatives, balancing technical feasibility, scope, timelines, and stakeholder expectations.

* Establish delivery plans, resource plans, sprint cadences, and engineering KPIs to monitor progress, unblock teams, and ensure predictable outcomes.

* Collaborate with product owners, business stakeholders, and program teams to define roadmaps, resource needs, and prioritization of data products and platform enhancements.

* Serve as the escalation point for engineering blockers, architectural decisions, or trade-off discussions, driving resolution across teams.

* Ensure team compliance with enterprise data modeling, documentation, and metadata standards.

* Standardize technical documentation practices for data models, transformation logic, and platform operations to promote reuse and transparency.

* Embed lineage, data dictionary, platform metadata integration, and architectural documentation into delivery workflows using tools such as Alation, Collibra, and schema registries.

* Partner with governance, compliance, and security teams to integrate policy-as-code frameworks, RBAC, and data governance policies into engineering execution.

* Drive implementation of data quality frameworks embedded within orchestration and transformation pipelines.

* Establish SLAs, observability dashboards, and automated validation rules for critical data assets and domain-specific pipelines.

* Lead root cause analysis and continuous improvement for data quality incidents, latency, pipeline failure, ensuring traceability across ingestion, enrichment, and delivery layers.

* Collaborate with technology and business teams to operationalize trusted data practices and ensure alignment on quality definitions and expectations.

* Contribute to shaping data domain strategy by aligning engineering execution to enterprise priorities and architectural principles.

* Partner with product, technology, business and architecture leaders to define roadmaps that advance data maturity, platform scalability, and solution interoperability.

* Champion platform evolution initiatives such as self-service enablement, AI/ML readiness, and composable data product design.

* Provide input to the enterprise architecture council on patterns, trade-offs, and emerging technologies to guide platform modernization.

* Build trusted relationships with product owners, domain leaders, and business stakeholders enterprise domains such as across marketing, supply chain, customer, and store operations.

* Present project status, technical trade-offs, and platform health to both technical and non-technical audiences with clarity and confidence.

* Represent engineering in business domain forums, roadmap sessions, providing insight into data platform capabilities, gaps, and enhancement opportunities.

* Oversee the delivery of foundational data assets, curated datasets, and semantic layers to drive business outcomes and analytics adoption.

* Guide the team in building unified metrics layers, semantic data models, and analytical datasets aligned with business reporting and decisioning needs.

* Partner with data science and ML engineering to ensure the semantic layer and metric…