Senior Data Engineer

Bold Thinking. World Changing. At SkyWater, our ingenuity helps improve lives around the world by manufacturing U.S. made semiconductors that are essential ingredients of modern life. Automotive safety enhancements, life-saving medical devices, consumer electronics and American security require semiconductors. Working in our Minnesota headquarters, Florida, or Texas location — employees join together to improve the world.


Explore what’s possible. Joining our U.S. - based team means contributing to and learning about the commercialization of some of the most exciting technologies the world has ever seen. We are turning “science fiction” into everyday reality through technologies such as superconducting, 3D integrated circuits or computer chips, carbon nanotubes, photonic logic devices, micro electro-mechanical systems and other emerging device topologies. We manufacture products for aerospace and defense, medical, automotive, consumer and industrial markets, to name a few. Our customers include emerging leaders who rely on our intellectual property security and quality manufacturing services.


Step into the future. SkyWater’s values of Integrity, Excellence, Collaboration, Empowerment and Growth Mindset guide us to cultivate an empowered, learning environment. We also invest in developing highly skilled, dedicated employees — and employees who are entering the workforce for the first time, from the military, and a variety of educational backgrounds.


Are you bold thinking? Find your place on our team and help us change the world!


About the Role
We are looking for a highly skilled and experienced Senior Data Engineer to join our growing AI, Data, and Analytics team. In this role, you will own the data platform roadmap and architecture, design and deliver scalable, secure, and reliable data products that power analytics and operational decision-making. You will operate with significant autonomy, partnering with stakeholders across the business to translate strategy into execution, define standards, and continuously improve data capabilities, quality, and governance.
Key Responsibilities
• Own the data architecture and platform strategy (12–24 month horizon), including reference architecture, technology selection, and standards for ingestion, orchestration, transformation, storage, and serving.
• Lead technical discovery and solution architecture for new domains and use cases, translating ambiguous requirements into scalable designs and clear execution plans.
• Design, implement, and maintain scalable and efficient data pipelines using modern data stack tools (e.g., Airflow, dbt, Spark).
• Own end-to-end ETL/ELT patterns to ingest data from a variety of structured and unstructured sources (APIs, databases, third-party systems).
• Design and optimize data models (warehouse/lakehouse) for analytics and operational use, focusing on trusted, governed datasets and metrics.
• Work across domains to gather requirements, architect, and build new/novels solutions to support the appropriate business requirements.
• Work with business stakeholders to develop/build reporting dashboards.
• Collaborate with architects, engineers, and analysts to design and optimize data models for analytics and operational use.
• Set data quality standards, including automated testing, monitoring, anomaly detection, and documentation practices.
• Set reliability targets (SLAs/SLOs) for critical data products; lead incident response, root-cause analysis, and post-incident improvements.
• Influence and align across Engineering, IT, Security, and Compliance to ensure solutions meet access control, auditability, privacy, and regulatory requirements.
• Optimize performance of databases and data workflows to support real-time and batch processing.
• Mentor junior engineers and contribute to team development and best practices.
• Work across teams to identify, support, and troubleshoot as needed.
• Create reusable templates, playbooks, and reference implementations to accelerate onboarding and consistent delivery across teams.
• Implement data governance practices, including data security, privacy, and compliance.
Qualifications
• Master’s degree in Computer Science, Engineering, Information Systems, or a related field.
• 5+ years of experience in data engineering, with a strong focus on building and maintaining data infrastructure at scale.
• Proficient in Python, SQL, and one or more big data technologies (e.g., Spark, Hadoop, Kafka).
• Experience with managing cloud IAM permissions, data access controls, and key management.
• Experience with cloud data platforms such as AWS (Redshift, S3, Glue), GCP (BigQuery, Dataflow), or Azure.
• Hands-on experience with orchestration tools like Airflow or Prefect.
• Experience with inf