Staff Analytics Engineer

Responsibilities: 

• Proactively identify and address challenges in semiconductor manufacturing and data automation using strong problem-solving and critical thinking skills. 

• Deliver customer-focused, impactful data solutions by leveraging sound judgment, creativity, and collaboration with cross-functional teams, including data analysts, product managers, data scientists, and engineers. 

• Gather and translate business and functional requirements into scalable, robust solutions aligned with Data Architecture guidelines for manufacturing and test environments. 

• Design, develop, and optimize data pipelines and products to process critical manufacturing data, leveraging scripting and programming skills to efficiently parse, extract, and transform diverse formats such as STDF files, wafer metrology data, wafer sort results, final test data, tool logs, MES data, FDC data, KLARF files, and visual inspection images, ensuring seamless integration into data pipelines. 

• Enhance data quality and completeness by partnering with source owners across MES, SPC, and test systems, ensuring consistent integration and accessibility of data from front-end fabs and back-end assembly/test sites. 

• Conduct root cause analysis on data delivery and quality issues, ensuring adherence to SLA requirements and maintaining high data reliability. 

• Build expertise in key strategic data sources, data lineage, and manufacturing automation systems, including MES platforms like Camstar/OpCenter and PROMIS, SPC tools, and advanced analytics platforms. 

• Drive automation and optimization of manufacturing processes, including wafer sort, wafer metrology, and final test operations, leveraging advanced analytics and data engineering best practices. 

• Standardize and streamline data flows from tool logs, wafer tracking systems, and test equipment for accurate reporting and analysis. 

• Facilitate the integration of manufacturing and test data into cloud-based modern data platforms, leveraging tools such as Snowflake, Azure Data Lake, DBT, and Airflow to enable scalable storage, processing, and analytics while ensuring alignment with enterprise data architecture and governance standards. 

Qualifications: 

• 10+ years of experience in Data Engineering, Data Science or Data Analytics roles. 

• Experience in semiconductor industry highly preferred. 

• Degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field.  

• Experience with data modeling, ELT processes, medallion architecture, open file formats and data integration techniques. 

• Proficiency in SQL and Python. 

• Experience with big data technologies (Spark, Hadoop, etc.), containerization (Kubernetes), and cloud technologies like Snowflake, Azure, DBT, Airflow/Dagster, etc. 

• Experience with cloud-based data pipeline observability tools such as Splunk Observability Cloud, Grafana Cloud, Datadog Observability Platform, etc. 

• Strong knowledge of statistical methods and techniques and how to apply them to identify anomalies and root causes for anomalies.  

• Experience building guided analysis flows utilizing advanced analytic techniques.  

• Strong proficiency in analytics & visualization tools such as Spotfire, Exensio, Klarity ACE,  DataAiku, Alteryx, PowerBI, Tableau. 

• Experience delivering solutions based on pandas, Seaborn, Matplotlib, Scikit-learn. 

• Experience delivering solutions leveraging Pytorch, MLFlow.