Data Engineering Manager

Relocation to Springfield, MO required

Required Skills and Experience:

• 10 years of overall Data Engineering experience
• Proven experience in Data Engineering leadership leading teams
• Retail domain experience (eCommerce, Supply Chain, Customer Marketing, Digital Retail, Merchandising, Transportation, etc)
• Strong technical and architectural knowledge in: Cloud platforms: GCP preferred with BigQuery (Redshift, Databricks, Azure Synapse, or similar)
• Languages: Python
• Orchestration tools: Airflow, Prefect, Composer
• ETL tools: Informatica, Google-native solutions
• Streaming: Kafka Background in retail, e-commerce, digital, or marketing analytics; experience with customer measurement highly desirable.
• Ability to manage hybrid teams (onsite and offshore) and deliver complex projects.

We are seeking a Data Engineering Manager to lead initiatives in the digital and retail marketing domain, focusing on customer data, analytics, and foundational metrics. This role requires strong technical expertise, leadership skills, and the ability to deliver scalable data solutions that enable advanced analytics and reporting. Key Responsibilities

• Lead and manage a team of data engineers, ensuring timely delivery and execution of projects.
• Design and build data pipelines integrating multiple sources into platforms like Snowflake and BigQuery, transforming data for dashboards, reports, and deep analytics.
• Partner with marketing and digital teams to develop solutions for:
• Customer Data Platform (CDP) expansion and orchestration.
• Warranty platform onboarding.
• Foundational metrics (acquisition cost, funnel analysis, campaign performance).
• Drive projects such as:
• Digital reporting and analytics (Google Analytics, Quickstream data).
• Customer behavior analysis (traffic drivers, abandoned cart signals, engagement metrics).
• Marketing campaign measurement and optimization.
• Collaborate cross-functionally with data science, marketing, e-commerce, and product teams.
• Provide architectural guidance and ensure best practices in data engineering.