Engineer Data Engineering & Analytics

We are seeking great talent to help us build The DNA of tech.® Vishay manufactures one of the world's largest portfolios of discrete semiconductors and passive electronic components that are essential to innovative designs in the automotive, industrial, computing, consumer, telecommunications, military, aerospace, and medical markets. We help the world's most in-demand technologies come to life. Every day our products touch your life and the lives of people across the world, though you likely do not know it.

Come join us and help us build The DNA of tech.™ Vishay Intertechnology, Inc. is a Fortune 1,000 Company listed on the NYSE (VSH). Learn more at

Do you want to help us build the DNA of tech.? Vishay Pune, India is currently seeking applicants for an Engineer-Data Engineering & Analytics

Job Location
- Magarpatta ,Pune

The Data Engineer is responsible for designing, building, and operating scalable, secure, and high‑quality data pipelines and data platforms across the enterprise application landscape. This role executes the enterprise data strategy defined by the Enterprise Data Architect, translating architecture standards, governance rules, and target‑state designs into reliable, production‑grade data solutions that support analytics, AI/ML, operational reporting, and downstream integrations. The Data Engineer works hands‑on with SAP systems, Salesforce CRM, Pricing and Quoting platform, MES platforms, middleware/integration tools, and cloud data platforms, ensuring enterprise data is trustworthy, accessible, and compliant.

• Data Pipeline Engineering & Integration
  • Design, implement, and maintain batch and near‑real‑time data pipelines from: SAP ECC / S/4
    
    HANA (IDocs, extractors, CDS views, APIs),Salesforce CRM, Pricing and quoting platform, Multiple MES systems , External and third‑party data sources
  • Implement ETL/ELT pipelines using enterprise‑approved tools and cloud services
  • Ensure reliable orchestration, monitoring, error handling, and recovery of data flows
  • Optimize performance, scalability, and cost efficiency of data ingestion processes
• Enterprise Data Platform Development
  • Build and operate enterprise data platforms such as:
    Cloud data warehouses / data lakes MS Azure and SAP Business Data Cloud or related data product,SAP BW / analytics backends,
  • Implement data models (raw, curated, semantic layers) aligned with:
    Enterprise canonical models
  • Business domains (Sales, Finance, Operations, Manufacturing, Supply Chain)
  • Enable analytics, reporting, AI/ML, and self‑service BI use cases
• Data Quality, Reliability & Observability
  • Implement data quality controls, validation rules, reconciliation checks, and alerts. Monitor data freshness, completeness, accuracy, and consistency
  • Support root cause analysis for data issues across source systems and pipelines
  • Ensure SLAs are met for business‑critical datasets
• Data Governance & Security Implementation
  • Enforce data governance standards defined by the Enterprise Data Architect, including:
    Data ownership and stewardship models,Master data alignment (customer, product, pricing, supplier)
  • Metadata, lineage, and documentation standards
  • Implement security, privacy, and compliance controls:
    Role‑based access control (RBAC),Data masking and encryption,GDPR and regulatory requirements
  • Support audits and compliance assessments related to enterprise data
• Collaboration with Enterprise Architecture & Stakeholders
  • Work closely with the Enterprise Data Architect to:
    Translate architecture blueprints into implementable solutions,Contribute to roadmap execution and platform evolution
  • Collaborate with:
    Application teams (SAP, Salesforce, MES)
    Integration teams (e.g., Mule Soft, SAP BTP Integration Suite)
    Analytics, BI, and Data Science teams
    
    Business stakeholders and process owners
  • Provide technical input to project design, demand intake, and solution reviews
• Dev Ops, Automation & Best Practices
  • Apply Data Ops and Dev Ops practices: CI/CD for data pipelines
    
    Infrastructure‑as‑code (IaC) where applicable
    
    Version control and automated testing
  • Document pipelines, models, and operational procedures
  • Continuously improve reliability, performance, and maintainability

• Bachelor's or Master's degree in Computer Science, Engineering, Data Engineering, or related field

• Strong hands‑on experience in data engineering (typically 5‑8+ years)
• Solid experience with:
  • SAP data sources (ECC/S4 tables, CDS views, extractors, APIs)
  • Salesforce data model and APIs
  • Enterprise integration platforms (e.g., Mule Soft, SAP BTP Integration Suite)
• Proficiency in:
  • SQL (advanced)
  • Python, Spark, or equivalent data processing frameworks
• Experience with:
  • Cloud data warehouses and data lakes
  • Dimensional and domain-oriented data modeling
  • Data quality and observability frameworks
  • Developing data ingest and semantic models within the data lake
• Familiarity with:
  • Manufacturing and MES data structu