Data Analyst - Telemetry & Engineering Data

Role Requirements The Data Analyst will support engineering and operational teams by analysing telemetry and sensor data generated from complex engineering systems. The role involves identifying trends, correlations, and anomalies across operational datasets and presenting insights through dashboards and visualisations. While the role sits within an aerospace engineering environment, experience analysing telemetry or sensor data from manufacturing, industrial systems, industrial IoT, or other engineering domains is highly relevant. The focus is on practical data analysis, statistical awareness, and communicating insights clearly through dashboards. Key Responsibilities Sensor Data Analysis • Analyse telemetry and operational datasets generated by complex engineering systems. • Identify trends, correlations, and anomalies across performance metrics. • Support engineers and operational teams in investigating performance questions using data-driven analysis. • Work with large datasets representing system performance over time. Example datasets may include: • Equipment performance telemetry • Sensor readings from industrial systems • Operational metrics from manufacturing equipment • Environmental and system performance data (In the aerospace context this may include engine, airframe, or flight sensor data.) Statistical & Analytical Techniques (Working Knowledge) • The analyst should have an awareness of common statistical approaches used in engineering data analysis, including: Correlation Analysis • Identifying relationships between variables to understand how different operational parameters move together. Example: understanding how temperature, load, or vibration correlate with system performance. Regression Analysis • Used to understand how one or more variables influence a particular outcome or performance metric. Time Series Analysis • Analysing data over time to identify trends, drift, cyclic behaviour, or performance degradation. Bayesian Approaches (Awareness) • Understanding how new observations can update probabilities or expectations based on prior knowledge. Basic Anomaly Detection • Identifying unusual patterns in telemetry or sensor data that may indicate faults or abnormal system behaviour. The role requires awareness of these techniques and when to apply them, rather than deep academic statistical expertise. Dashboarding & Data Visualisation • A key deliverable for the role is presenting insights clearly through dashboards. The analyst should be able to: • Build dashboards using Power BI or Tableau • Visualise operational and telemetry trends • Provide engineers and stakeholders with clear views of system performance • Enable drill-down investigation of anomalies or trends. Technology Environment Typical technologies may include: Microsoft / Azure data stack • Azure Data Lake • Azure Databricks • Azure Synapse • Azure Data Factory Data analysis • Python (Pandas / NumPy) • SQL Visualisation • Power BI or Tableau Stakeholder Engagement • The role requires working closely with engineering and operational stakeholders. The analyst should be able to: • Ask informed questions about system behaviour and operational context • Translate engineering questions into analytical approaches • Communicate insights clearly to non-data specialists. Analytical Awareness Candidates should demonstrate awareness of common pitfalls such as: • Confusing correlation with causation • Interpreting noisy sensor data incorrectly • Drawing conclusions from incomplete datasets • Overfitting analytical models. Desirable Skills Candidates may come from environments such as: • Manufacturing data analytics • Industrial IoT or sensor data analytics • Engineering or operational data analysis • Reliability or predictive maintenance analytics • Aerospace or defence analytics.