AWS Data Engineer – Manufacturing & Unified Namespace (UNS)
About the role
Job Summary
We are seeking an experienced AWS Data Engineer to design, build, and support cloud-based data pipelines for manufacturing and industrial systems. The ideal candidate will have strong expertise in real-time data engineering, AWS cloud services, and Unified Namespace (UNS) architectures to enable scalable data integration across shop-floor systems, Digital Twin platforms, and analytics solutions.
This role will focus on ingesting, standardizing, and delivering manufacturing data from presses, sensors, CBM systems, MES platforms, and manual inputs into enterprise data and analytics environments.
Key Responsibilities
- Design and implement cloud-based data ingestion pipelines from manufacturing systems including PLCs, press controllers, CBM systems, and MES platforms.
- Publish and consume industrial data through MQTT-based Unified Namespace (UNS) architectures.
- Build scalable real-time, near real-time, and batch data pipelines on Amazon Web Services.
- Transform and model raw machine and operational data into contextualized, asset-based datasets.
- Integrate manufacturing data into Digital Twin, analytics, reporting, and KPI calculation platforms.
- Ensure high levels of data quality, reliability, observability, and pipeline performance.
- Collaborate with engineering, manufacturing, and analytics teams to support industrial data initiatives.
- Maintain documentation and support best practices for industrial data architecture and governance.
Required Qualifications
- Strong hands-on experience with Python for data engineering and pipeline development.
- Proficiency in SQL for analytics, transformations, and data processing.
- Experience with stream and batch processing architectures.
- Strong understanding of MQTT publish/subscribe communication patterns.
- Experience designing topic namespaces aligned with industrial asset hierarchies.
- Hands-on experience implementing or consuming Unified Namespace (UNS) architectures.
- Experience working with JSON, Avro, and Parquet data formats.
- Strong analytical, troubleshooting, and problem-solving skills.
Preferred Qualifications
- Experience with manufacturing or industrial IoT environments.
- Exposure to Digital Twin architectures and industrial analytics platforms.
- Familiarity with Infrastructure as Code (IaC) tools such as Terraform or CloudFormation.
- Experience with AWS-native data services and cloud data architectures.
- Understanding of MES, CBM, and shop-floor integration concepts.
Key Skills & Competencies
- AWS Cloud Data Engineering
- MQTT & Unified Namespace (UNS)
- Real-time & Streaming Data Pipelines
- Industrial IoT & Manufacturing Data
- Python & SQL Development
- Data Modeling & Transformation
- Data Quality & Observability
- Analytics & Digital Twin Integration
This engagement is managed end-to-end by Querentia. Our recruiters give you honest feedback at every stage, prepare you for the interview, and support a smooth onboarding once you land the offer.