Asad Aslam
Data Engineer @Siemens
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WORK HISTORY
Data Engineer @Siemens
Munich, DE
Part of the supply chain visibility team at Siemens AG, building internal data engineering and Gen AI tools for supplier mapping and supply chain analytics.• Build Python ETL pipelines with GitLab CI/CD, using a Sentence Transformers (SBERT) model to automatically match supplier names from multiple source systems, reducing manual mapping by 50%• Develop and deploy a Streamlit application on Azure for reviewing and managing over supplier name and location mappings, reducing manual correction effort by roughly 30–40%• Manage Snowflake data warehouse operations, optimizing ELT workflows using SQL to improve pipeline efficiency for enterprise-scale supply-chain analytics• Integrate external REST APIs to automatically enrich supplier and location records across entries, replacing manual lookup workflows with fully automated real-time data enrichment• Develop and maintain Neo4j Cypher query templates for supply-chain data retrieval, enabling structured graph queries across supplier networks, risk tracking, and component tracing• Build and deploy a natural-language supply-chain chatbot in Python using a two-tier rule-based and Azure OpenAI fallback engine, deployed as an Azure ML Managed Online Endpoint
EDUCATION
FAU Erlangen-Nürnberg
Master of Science - MS, Data Science
University of South Asia
Bachelor of Science - BS, Computer Science
ABOUT ASAD ASLAM
Over the past four years at Siemens, I have built production ETL/ELT pipelines in Python, managed Snowflake data warehouse operations, and deployed Gen AI solutions on Azure. My work sits at the intersection of data engineering, analytics, and applied AI, with a focus on turning messy supplier and supply-chain data into tools that people actually use every day.At Siemens AG in Munich, I built Python ETL pipelines running on GitLab CI/CD that use a Sentence Transformers model to automatically match supplier names across multiple source systems, cutting manual mapping work by 50%. I also developed and deployed a Streamlit application on Azure where teams review and manage over supplier name and location mappings, which reduced manual correction effort by roughly 30–40%. On the AI side, I built and deployed a natural-language supply-chain chatbot that converts questions into Neo4j Cypher queries using a two-tier rule-based and Azure OpenAI fallback engine, running as an Azure ML Managed Online Endpoint.Before that, at Siemens Mobility in Erlangen, I designed Power BI dashboards for real-time KPI tracking used across three operational teams, built ETL workflows with Power Query pulling from six different data sources, and automated reporting processes that eliminated 10–15 hours of manual work every week.My day-to-day stack includes Python, SQL, Snowflake, dbt, Neo4j, Azure, Docker, GitLab CI/CD, Streamlit, Power BI, LangChain, and Azure OpenAI. I hold an MSc in Data Science from FAU Erlangen-Nürnberg and a BSc in Computer Science, along with certifications from Neo4j, AWS, Microsoft, Google Cloud, and Mendix.Portfolio: asadaslam.techOpen to Data Engineer, AI Engineer, and Analytics Engineer roles across Germany. Connect or message me directly.
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