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

• Bachelors 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.