Data Engineer

We are looking for a senior-level Data Engineer to shape and deliver a scalable data platform in Kalamazoo, Michigan. This role combines strategic architecture with hands-on engineering, creating reliable data products that support reporting, advanced analytics, and AI-driven solutions. The ideal candidate will build secure, multi-tenant data capabilities with strong attention to privacy, governance, and long-term platform quality.

Responsibilities:
• Lead the design of a modern data platform that supports ingestion, transformation, storage, and consumption across analytical and operational use cases.
• Build and maintain robust batch and streaming pipelines that move data from relational systems, object storage, document databases, and event sources into centralized platforms.
• Define data architecture standards, modeling approaches, and engineering practices that improve consistency, reliability, and scalability across the organization.
• Create multi-tenant data solutions with strong isolation controls, secure access patterns, and governance measures built into the platform design.
• Develop data models and serving layers that enable enterprise reporting, self-service analytics, and AI or machine learning workloads.
• Evaluate cloud-based data services, processing frameworks, and warehouse technologies to ensure the platform meets performance, cost, and security expectations.
• Partner with product, engineering, and leadership teams to explain technical decisions, highlight risks, and align platform investments with business priorities.
• Oversee external vendors and implementation partners by reviewing recommendations, challenging misaligned approaches, and enforcing internal data standards.

Requirements

• 10+ years of experience in data engineering, including ownership of large-scale architecture decisions in production environments.
• Strong expertise with Python and modern data processing technologies such as Apache Spark, Kafka, Hadoop, or similar platforms.
• Proven experience designing both batch and real-time data pipelines for cloud-based ecosystems, including AWS services and enterprise data warehouses.
• Deep understanding of data modeling for transactional and analytical workloads, including multi-tenant design principles and access control strategies.
• Hands-on experience with platforms such as Snowflake, Redshift, graph databases, and document-oriented data sources.
• Solid knowledge of data governance, privacy requirements, and secure handling of sensitive information in shared environments.
• Experience with ETL and transformation frameworks, including testing, documentation, and deployment practices within CI/CD workflows.
• Demonstrated ability to communicate architecture strategy and technical tradeoffs clearly to senior leaders, business stakeholders, and external partners.