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, BigQuery, 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 stores are consistent, reusable, and extensible for downstream use cases.
• •Oversee integration of business logic into transformation layers to support real-time, self-service, and LLM-driven analytics.
• •Enable rapid insights through well-structured feature stores, reusable dbt models, and aligned dimensional views.
• •Lead change management efforts during platform migrations, architectural shifts, and new technology onboarding.
• •Drive cultural adoption of modern practices such as declarative data modeling, data contracts, universal semantic layer, streaming-aware designs, and platform-as-a-product mindset.
• •Stay informed on emerging data technologies to shape future-state capabilities and ensure team readiness for evolution.
• •Define and drive team development plans aligned with evolving platform and domain requirements, ensuring skill growth and succession planning.
• •Foster a culture of engineering excellence, agile delivery, collaboration, and accountability across the team.
• •Lead internal communities of practice, technical workshops, and user groups to drive and disseminate best practices.

• Skills:
• Required:

• •Google Cloud Platform knowledge and experience
• •Strong experience leading the design, development, and deployment of enterprise-grade data platforms and products (e.g., Snowflake, BigQuery, dbt, Airflow, Informatica, and Kafka/PubSub).
• •Proficient in programming languages such as Python and SQL with ability to review code, enforce engineering standards, and support performance optimization in batch and streaming pipelines.
• •Demonstrated understanding of data architecture principles, including medallion layering, data mesh design, and streaming architectures to support diverse use cases.
• •Experience implementing CI/CD for data pipelines, schema management workflows, and observability frameworks (e.g., data quality monitors, alerting dashboards, SLOs/SLAs).
• •Practical experience managing transformation and orchestration layers to deliver curated data assets, semantic layers, and ML feature stores aligned to consumption patterns.
• •Proficiency in metadata-driven engineering, including integration with Alation, Collibra, or similar tools for lineage, data catalogs, and policy enforcement.
• •Proven ability to collaborate with product owners, business stakeholders, and domain SMEs to prioritize data needs, translate requirements into executable engineering tasks, and ensure business value delivery.
• •Deep understanding of enterprise business processes across functional areas (e.g., supply chain, customer, store ops, finance) and how data platforms can accelerate decision-making.
• •Ability to evaluate business cases, clarify problem statements, and manage trade-offs between delivery timelines, data quality, performance, and platform sustainability.
• •Experience in leading the delivery of trusted, governed data assets that directly impact business reporting, operational workflows, and digital transformation