Description:

Siemens Healthineers is hiring a Senior Data Engineer to support, optimize, and scale its high-concurrency Customer Service Advanced Technology (CS-AT) platform in Bangalore. In this production-critical big data engineering role, you will take ownership of the data ingestion, parsing, and analytical storage architecture that processes decades (15–20 years) of massive historical and real-time medical machine logs and IoT telemetry data. Operating as a hybrid engineer handling both development and operations (Data Product Ownership mindset), your primary focus will be transforming unstructured, high-volume hardware log files into structured data models using Azure Data Explorer. Your work directly feeds downstream predictive AI systems (such as OneAI and MI Log Interpreter) to enable zero-downtime, proactive medical equipment maintenance across global hospital networks.

Key Technical Skills:

Kusto Query Language (KQL), Azure Data Explorer (ADX), Azure Data Factory (ADF), Databricks, Apache Spark, Delta Lake, Python, C#, Azure Functions, Azure Event Grid, Log Parsing & Regex Pattern Extraction, High-Volume Telemetry Systems, Pipeline Optimization & Monitoring

Requirements:

• 5 to 6 years of verified professional experience operating as a dedicated data engineer or telemetry platform engineer building large-scale, distributed data architectures.
• Expert-level proficiency writing, tuning, and scaling multi-layered Kusto Query Language (KQL) scripts, alongside hands-on engineering experience administering Azure Data Explorer (Kusto) environments.
• Deep development command over distributed data processing, including configuring structured pipelines inside Databricks/Spark and managing storage logic utilizing Delta Lake.
• Reliable object-oriented and scripting capability using Python and C#, paired with direct experience deploying event-driven data workflows through Azure Functions and Azure Event Grid.
• Proven track record building end-to-end cloud-native ETL/ELT pipelines using Azure Data Factory to ingest high-volume, continuous historical and streaming datasets.
• Advanced analytical background in machine log parsing, semantic telemetry mapping, string manipulation, and root-cause anomaly extraction.
• Strong pipeline monitoring, stability optimization, and automated alerting skills, aligned with strict document guidelines under formal engineering standards.
• Direct exposure to machine log analytics, IoT frameworks, device telemetry streams, predictive maintenance business cases, or automated data science workflows.