Data Engineer

Description

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.

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