Amphenol tcs is the leader in high-speed, high-density connection systems, designing and manufacturing the industry's leading high-speed, high-density connectors and backplane systems. amphenol tcs solves system design challenges with integrated interconnect solutions for application in the networking, communications, storage, and computer server markets. Amphenol tcs was founded in 1968 as a division of teradyne, inc. and acquired by amphenol corporation in december of 2005. amphenol corp. is the world?s third largest connector company, producing electronic and fiber optic connectors, cable, and interconnect systems for applications converging technologies of voice, video and data communications, industrial / automotive and military / aerospace. A history of innovation : Amphenol has 70+ year history of innovation and a leadership position in all markets served. atcs engineers in the field and in product development work closely with our customers to understand their technology needs now and in the future.
Responsible for designing, developing, and maintaining business intelligence and analytics applications that embed product quality intelligence into key lifecycle quality decisions. Planned, organized, and coordinated data and reporting activities to transform manufacturing, test, and field information into actionable insights. Collaborates with product quality, engineering, reliability, test, supply chain, and field quality teams to monitor performance, detect early risk indicators, and support root cause analysis (RCA). Responsible for operationalizing quality KPIs (yield, defect rates, fallout, escapes, reliability indicators, and customer-reported failure trends) through standardized datasets, dashboards, and analytics-ready models. Additionally, proposes and contributes to advanced analytics and machine learning opportunities to improve quality prediction, classification, and anomaly detection.
Design and deliver BI and analytics solutions providing visibility into product quality performance across the lifecycle.
Translate manufacturing, test, supply chain, and field data into analytics-ready datasets through effective data modeling and data integration collaboration.
Define, standardize, and operationalize product quality KPIs (yield, defect rates, fallout, escapes, reliability indicators, and customer failure trend metrics).
Develop dashboards, reports, and visual analytics aligned with quality, engineering, and operations user needs.
Proactively analyze trends and anomalies to identify early indicators of quality risk (leading indicators such as early yield shifts, test fallout precursors, and field return precursors).
Partner with cross-functional teams (manufacturing, design, reliability, test, supply chain, field quality) to frame quality challenges in analytics and data terms.
Support root cause analysis (RCA) and systemic issue identification using structured data exploration.
Collaborate with global data/development teams to specify, review, and validate ETL and data transformation logic supporting analytics use cases.
Validate data accuracy, completeness, and fitness for quality decision-making.
Bachelor’s degree in Information Systems, Computer Science, Engineering, or a related technical field.
Minimum 2 years of experience in Business Intelligence, Analytics, or data-driven roles.
Experience supporting or working within product quality, manufacturing quality, reliability engineering, test, or field quality environments, or proven experience analyzing quality-related data.
Strong understanding of data warehousing concepts, relational data modeling, and analytical best practices.
Practical experience with ETL and data integration processes, including requirements definition and transformation validation (partnering with ETL developers).
Proficiency in SQL and data exploration; experience with Python is a plus.
Experience developing reports, dashboards, and visual analytics using tools such as Power BI or Business Objects.
Ability to translate business and quality requirements into scalable analytical solutions.
Strong analytical, problem-solving, and structured thinking skills.
Experience working with global or distributed teams.
Clear communication skills with both technical and non-technical audiences; ability to bridge engineering, quality, and data science perspectives.
