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 programsHistorian modernization or migration projectsOil & Gas, Energy & Utilities, Manufacturing, Refining, Mining, or Chemicals environmentsPredictive maintenance, asset performance management, operational intelligence, or digital twin initiativesOT/IT integration and cloud-based industrial analytics platforms Key Competencies TDengine Platform Engineering Python Development 3. Time-Series Data Modeling 4. Historian Integration 5. Performance Optimization 6. Data Pipeline Automation 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.