Job Summary
Design and build production-grade data pipelines for OEE metrics Connected Worker events and Golden Batch analytical data
Develop data models for manufacturing analytics including batch records equipment telemetry and worker activity data
Implement both real-time streaming and batch data processing for Trio Line operational data
Build and maintain data lake/warehouse structures optimized for GMSx analytical use cases
Responsibilities
Design and build production grade data pipelines for OEE metrics Connected Worker events and Golden Batch analytical data.
Develop data models for manufacturing analytics including batch records equipment telemetry and worker activity data.
Implement both real time streaming and batch data processing for Trio Line operational data.
Build and maintain data lake and data warehouse structures optimized for GMSx analytical use cases.
Ensure data quality through validation rules anomaly detection and data integrity checks for manufacturing data.
Implement data lineage tracking metadata management and audit trail capabilities compliant with GxP requirements.
Optimize data pipeline performance to meet production throughput and latency requirements.
Collaborate closely with the Senior Techno Functional Consultant to align data architecture with business requirements and use case definitions.
Required Qualifications and Experience
5 plus years of hands on data engineering experience with Apache Spark Python and SQL.
Experience with real time data streaming technologies such as Kafka Streams Spark Streaming or equivalent platforms.
Cloud data platform expertise including Azure Data Factory Azure Databricks or equivalent cloud native data services.
Strong understanding of manufacturing data models time series data and equipment telemetry patterns.
Knowledge of data governance frameworks data lineage tracking and metadata management.
Experience with data quality frameworks and automated data validation pipelines.
Familiarity with GxP data integrity requirements including ALCOA Plus principles electronic records and audit trails.
Ability to work in cross functional teams and translate data requirements from business stakeholders into technical solutions.
Preferred Qualifications
Experience with pharmaceutical manufacturing data including batch records process parameters and equipment logs.
Familiarity with Unified Namespace data architecture and Manufacturing Data Fabric concepts.
Experience with Delta Lake Apache Iceberg or similar lakehouse technologies.
Knowledge of statistical process control and Golden Batch analysis methodologies.
Experience with data visualization tools such as Power BI and Grafana for manufacturing dashboards.
About Cognizant:
Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization’s unique context into technology systems that amplify human potential, drive tangible outcomes and keep global enterprises ahead in a fast-changing world. See how at cognizant.ai or @cognizant.
Additional employment information
Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview.
Cognizant is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, state or local laws.
