TDengine Platform Engineer

  • Melbourne, Victoria, Australia
  • Full-Time
  • On-Site
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Job Description:

Role: TDengine Platform Engineer
Location: Melbourne, VIC
Experience: 10+ years
Job Type: Permanent

Role Summary:
We are seeking an experienced TDengine Platform Engineer with strong Python development skills to design, implement, integrate, and support time-series data solutions for industrial historian and Industrial IoT workloads. The role will focus on high-volume operational data ingestion, time-series modeling, API-based integrations, automation, and analytics enablement across OT and enterprise systems.

Key Responsibilities

  • Design, deploy, configure, and support TDengine databases for industrial time-series and historian workloads.
  • Develop Python-based scripts, services, and automation utilities for data ingestion, transformation, validation, and analytics.
  • Create and optimize time-series schemas, super tables, tags, retention policies, and query patterns for high-volume sensor datasets.
  • Build real-time and batch data pipelines from OT/historian sources into TDengine and downstream analytics platforms.
  • Integrate TDengine with SCADA, PLC, OPC-UA, MQTT, historian systems, enterprise applications, and cloud data services.
  • Develop REST APIs, connectors, and microservices to expose operational data securely to business and analytics consumers.
  • Troubleshoot performance, ingestion, connectivity, query latency, data quality, and platform availability issues.
  • Implement monitoring, alerting, backup, recovery, access control, and operational support procedures.
  • Support dashboards, KPI reporting, predictive maintenance, anomaly detection, and operational intelligence use cases.
  • Prepare technical documentation, design notes, runbooks, support procedures, and knowledge articles.

Required Technical Skills Skill Area Expected Capabilities:


TDengine Platform TDengine database administration, TDengine SQL, super tables, time-series data modeling, retention policies, clustering, high availability, performance tuning, stream processing, subscriptions.

Python Development Python scripting and application development, Pandas, NumPy, REST APIs, FastAPI/Flask, JSON/XML handling, automation, error handling, logging, reusable data utilities.

Data Engineering ETL/ELT, real-time and batch processing, data validation, transformation, reconciliation, metadata handling, time-series aggregation, data quality governance.

Industrial Integration OPC-UA, MQTT, SCADA, DCS, PLC data ingestion, historian integration, sensor data pipelines, OT/IT integration patterns.

Cloud & DevOps Linux basics, Docker, Kubernetes awareness, Git, CI/CD, Azure/AWS integration patterns, monitoring and operational support.

Analytics Enablement Power BI or equivalent dashboards, time-series analytics, feature engineering, predictive maintenance, anomaly detection, operational reporting.

Qualifications & Experience

  • Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, or related discipline.
  • 5+ years of experience in database engineering, historian platforms, industrial data platforms, or time-series data systems.
  • Hands-on experience with TDengine or comparable time-series databases such as InfluxDB, TimescaleDB, OpenTSDB, or PI System.
  • Strong Python design, development, debugging, and automation skills.
  • Experience working with high-volume sensor, machine, plant, or operational datasets.
  • Good understanding of industrial communication protocols and OT data acquisition patterns.
  • Strong analytical, troubleshooting, stakeholder communication, and documentation skills.

Preferred Domain Experience

  • Industrial IoT / Industry 4.0 programs
  • Historian modernization or migration projects
  • Oil & Gas, Energy & Utilities, Manufacturing, Refining, Mining, or Chemicals environments
  • Predictive maintenance, asset performance management, operational intelligence, or digital twin initiatives
  • OT/IT integration and cloud-based industrial analytics platforms

Key Competencies

1. TDengine Platform Engineering
2. Python Development
3. Time-Series Data Modeling
4. Historian Integration
5. Performance Optimization
6. Data Pipeline Automation
7. Industrial Analytics
8. Troubleshooting & RCA
9. Stakeholder Communication

Suggested Interview Focus Areas

  • Experience designing schemas and super tables for industrial time-series data.
  • Python examples for ingestion, data quality validation, aggregation, APIs, and automation.
  • Approach to integrating OT sources such as OPC-UA, MQTT, SCADA, or existing historians.
  • Performance tuning, retention policy design, query optimization, and high-availability scenarios.
  • Ability to translate business use cases into reliable operational data solutions.