TDengine Platform Engineer

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

• TDengine Platform Engineering


• Python Development


• Time-Series Data Modeling


• Historian Integration


• Performance Optimization


• Data Pipeline Automation


• Industrial Analytics


• Troubleshooting & RCA


• 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.