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Posted 5 months ago

AWS Data Engineer – Manufacturing & Unified Namespace (UNS)

@ Querentia Inc.

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Toronto, Ontario

Contract
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Overview

Querentia Inc. is hiring a contract AWS Data Engineer to design and support cloud data pipelines for manufacturing and industrial systems. The work covers real-time and batch ingestion from PLCs, presses, CBM and MES platforms into Unified Namespace architectures and analytics.

Responsibilities:

  • Design cloud ingestion pipelines from PLCs, press controllers, CBM and MES
  • Publish and consume data through MQTT-based Unified Namespace
  • Build real-time, near real-time and batch pipelines on AWS
  • Model raw machine data into asset-based datasets
  • Integrate data into Digital Twin and KPI platforms

Requirements summary:

  • Hands-on Python for data engineering
  • Proficiency in SQL for transformations
  • Experience with stream and batch architectures
  • Knowledge of MQTT publish/subscribe patterns
  • Experience with JSON, Avro and Parquet formats

Tools & skills

AWSMQTTPythonSQLTerraformCloudFormation
Source and dates

Source type not verified · jsonld

Employer posting date
2026-05-27
First seen by our source
2026-10-26
Source record updated
2026-11-02

These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.

Job details

SenioritySenior
DepartmentData & analytics
Travelair: none · ground: none
Work settingOffice, Client sites
PositionMostly sitting
Physical demandsphysical low · computer high
LanguagesEnglish

Summary and details written by AI from the posting; the employer’s posting is the authority.

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Querentia Inc.

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FieldValue
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Job description

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

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