Job
Description
About Analog Devices Analog Devices, Inc. (NASDAQ: ADI ) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, and software technologies into solutions that help drive advancements in digitized factories, mobility, and digital healthcare, combat climate change, and reliably connect humans and the world. With revenue of more than $9 billion in FY24 and approximately 24,000 people globally, ADI ensures todays innovators stay Ahead of Whats Possible . Learn more at www.analog.com and on LinkedIn and Twitter (X) . Responsibilities As a Senior Manufacturing Data Engineer, you will collaborate with cross-functional teams, including data analysts, product managers, data scientists, and engineers, to deliver impactful data solutions for semiconductor manufacturing and automation. Translate business and functional requirements into scalable data solutions aligned with Data Architecture guidelines for manufacturing and test environments. Develop and maintain data pipelines to process manufacturing data, including formats such as STDF files, wafer metrology data, wafer sort results, tool logs, MES data, and FDC data, ensuring seamless integration into enterprise data systems. Write scripts and programs to parse, extract, and transform data from diverse sources, improving data accessibility and quality across systems. Support the integration of manufacturing and test data into cloud-based platforms such as Snowflake and Azure Data Lake, enabling advanced analytics and scalable processing. Contribute to the standardization and optimization of data flows from tool logs, wafer tracking systems, and test equipment to improve reporting and analytics accuracy. Partner with senior team members to enhance data quality and completeness across MES, SPC, and test systems. Perform root cause analysis on data issues, ensuring timely resolution and adherence to SLA requirements. Assist in building subject matter expertise in MES platforms like Camstar/OpCenter and PROMIS, SPC tools, and data analytics platforms. Participate in the development of frameworks for data pipeline observability, including alerting and monitoring systems. Qualifications 3+ years in Data Engineering, Data Science, or Data Analytics roles, with a preference for candidates with experience in semiconductor manufacturing. Degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field. Proficiency in SQL and Python. Experience with data modeling, ELT processes, and data integration techniques. Familiarity with big data tools (e.g., Spark, Hadoop), containerization (e.g., Kubernetes), and cloud platforms like Snowflake, Azure, and DBT. Exposure to analytics and visualization tools like Spotfire, Power BI, or Tableau. Basic understanding of statistical methods and techniques for anomaly detection and root cause analysis. Experience with scripting and programming for data parsing and transformation.
