Partner with process engineers, and integration teams to understand wafer experiment workflows—including etch, deposition, and metrology steps—and translate experimental and yield-learning objectives into actionable data and reporting requirements.
Design, develop, and maintain metrics, dashboards, and analytical reports focused on process tool performance, recipe execution, chamber health, and wafer-level traceability, and using ClickHouse, SQL, and Python.
Write detailed specifications for new data pipelines to onboard additional metrology tools, sensors, or process data sources, collaborating with data engineering to ensure proper schema design, transformations, and quality checks within the AWS/Kafka/ClickHouse stack.
Validate and reconcile data from disparate systems (MES, tool logs, recipe management, metrology platforms, yield databases) to ensure accurate linkage at the wafer, lot, run, and step level within the technical data warehouse.
Develop reusable Python scripts and SQL queries for ad-hoc and recurring analyses such as DOE evaluation, process window characterization, virtual metrology exploration, and wafer-to-wafer or lot-to-lot comparisons.
Work within established data governance and access controls to ensure accuracy, timeliness, and appropriate confidentiality of all engineering reports, dashboards, and experimental data.
Create and maintain documentation for datasets, table schemas, KPIs, and pipeline logic—including data lineage, business definitions, and usage examples tailored to process contexts.
Train and support application developers and data analysts in using the data warehouse, BI tools, and self-service dashboards; gather feedback to continuously improve data products and user experience.
Collaborate with data scientists and ML engineers to operationalize predictive models by ensuring required features and labels are available, accurate, and well-documented in the warehouse.
Create automated workflows for data ingestion, cleansing, and integration of large-scale process and metrology datasets, leveraging Python, ETL orchestration tools, and cloud services on AWS.
Assess evolving reporting needs in the context of R&D priorities and technology roadmaps; propose and deliver appropriate solutions ranging from quick ad-hoc analyses to production-grade dashboards.
Generate internal documentation, presentations, and technical reports summarizing experimental results, data quality assessments, and analytics insights for cross-functional stakeholders.
Ability to translate engineering workflows and business needs into data requirements and specifications.
Experience working directly with engineers, operations teams, or technical stakeholders.
Solid documentation, communication, and stakeholder engagement skills.
Experience designing and validating metrics, KPIs, and dashboards for operational or engineering environments.
Strong proficiency in SQL and Python for data analysis, automation, and pipeline support.
Experience with BI and visualization tools (Tableau, Power BI, Superset, Grafana, Looker, or similar).
Version control (Git), CI/CD for analytics, and data documentation tooling.
Bachelor’s degree in data science, computer science, engineering, or related field.
4+ years of experience in data analysis, preferably within a high-tech or manufacturing environment.
Demonstrated experience supporting business units with analytical solutions.
Strong attention to detail and organizational skills.
Excellent communication skills for presenting complex findings to stakeholders.
Ability to work independently and collaboratively in a fast-paced environment.
Proactive approach to process improvement and automation.
Software Development Process
JIRA, BitBucket
Demonstrates depth and/or breadth of expertise in own specialized discipline or field
Interprets internal/external business challenges and recommends best practices to improve products, processes or services
May lead functional teams or projects with moderate resource requirements, risk, and/or complexity
Leads others to solve complex problems; uses sophisticated analytical thought to exercise judgment and identify innovative solutions
Impacts the achievement of customer, operational, project or service objectives; work is guided by functional policies
Communicates difficult concepts and negotiates with others to adopt a different point of view
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