Description - External
- Data Product Engineer (8+ Years Overall Experience)
- Location: Bengaluru
- Employment Type: Full-Time
Role Purpose
Define and drive the data architecture required to integrate data across fragmented manufacturing, engineering and enterprise systems and establish a scalable, governed and reusable data foundation.
The role will design domain-oriented data structures and data products that enable downstream teams to build applications, analytics, AI/ML solutions and AI agents without repeatedly integrating directly with individual source systems.
Data Architecture
- Define the target data and integration architecture across multiple legacy and modern systems.
- Assess existing data sources, interfaces, dependencies and data flows.
- Design domain and canonical data models that provide consistent business semantics across systems.
- Define architecture for structured, semi-structured, unstructured and streaming data.
- Establish scalable Lakehouse and data-platform architecture.
Integration Architecture
- Define appropriate integration patterns across APIs, events, streaming, CDC, batch and ETL/ELT.
- Reduce point-to-point integrations and unnecessary data duplication.
- Define how data from manufacturing, planning, engineering, IoT/shopfloor and enterprise systems should be integrated.
- Establish reusable integration and interoperability standards.
Data Product Architecture
- Identify and define reusable data products based on business domains.
- Define ownership, interfaces/contracts, quality expectations, metadata and consumption patterns for data products.
- Design standardized consumption layers for applications, analytics, digital twins and AI.
- Enable data products to be consumed through APIs, events, Lakehouse tables and semantic layers as appropriate.
AI-Ready Data Foundation
- Define data architecture supporting AI/ML, GenAI, RAG and agentic applications.
- Enable structured and unstructured enterprise data for AI consumption.
- Define patterns for embeddings, vector stores, metadata and enterprise knowledge integration where required.
Governance & Standards
- Define standards for data quality, lineage, metadata, master/reference data, access control and data lifecycle.
- Work with security and platform teams to ensure compliance with enterprise data policies.
- Establish architecture principles and reusable patterns for development teams.
Technical Skills
- Strong experience in Data Architecture and Integration Architecture
- Data Lake / Lakehouse / Data Warehouse architectures
- Domain-driven data architecture and data products
- Microsoft Fabric / Azure / Databricks or comparable platforms
- SQL and good working knowledge of Python/PySpark
- API and event-driven architectures
- Kafka/Event Hubs or similar streaming technologies
- ETL/ELT and CDC patterns
- Data modeling and canonical models
- Data governance, lineage and metadata management
- Cloud security and access-control concepts
- Exposure to AI/ML and GenAI data architectures
Preferred Manufacturing Experience
- Manufacturing IT / OT landscapes
- MES, ERP, planning, quality or shopfloor systems
- IoT and equipment data
- Manufacturing master/reference data
- Production planning and engineering data
- Digital Twin / Factory Digitalization use cases
Expected Profile
- Approximately 8+ years of relevant experience, depending on the internal leveling model.
- Strong background in enterprise data architecture, integration or data-platform engineering.
- Ability to move between business/domain discussions and detailed technical architecture.
- Should be capable of hands-on prototyping and technical validation, not only architecture documentation.
Description - External
Job Description
- Data Product Engineer (8+ Years Overall Experience)
- Location: Bengaluru
- Employment Type: Full-Time
Role Purpose
Define and drive the data architecture required to integrate data across fragmented manufacturing, engineering and enterprise systems and establish a scalable, governed and reusable data foundation.
The role will design domain-oriented data structures and data products that enable downstream teams to build applications, analytics, AI/ML solutions and AI agents without repeatedly integrating directly with individual source systems.
Key Responsibilities
Data Architecture
- Define the target data and integration architecture across multiple legacy and modern systems.
- Assess existing data sources, interfaces, dependencies and data flows.
- Design domain and canonical data models that provide consistent business semantics across systems.
- Define architecture for structured, semi-structured, unstructured and streaming data.
- Establish scalable Lakehouse and data-platform architecture.
Integration Architecture
- Define appropriate integration patterns across APIs, events, streaming, CDC, batch and ETL/ELT.
- Reduce point-to-point integrations and unnecessary data duplication.
- Define how data from manufacturing, planning, engineering, IoT/shopfloor and enterprise systems should be integrated.
- Establish reusable integration and interoperability standards.
Data Product Architecture
- Identify and define reusable data products based on business domains.
- Define ownership, interfaces/contracts, quality expectations, metadata and consumption patterns for data products.
- Design standardized consumption layers for applications, analytics, digital twins and AI.
- Enable data products to be consumed through APIs, events, Lakehouse tables and semantic layers as appropriate.
AI-Ready Data Foundation
- Define data architecture supporting AI/ML, GenAI, RAG and agentic applications.
- Enable structured and unstructured enterprise data for AI consumption.
- Define patterns for embeddings, vector stores, metadata and enterprise knowledge integration where required.
Governance & Standards
- Define standards for data quality, lineage, metadata, master/reference data, access control and data lifecycle.
- Work with security and platform teams to ensure compliance with enterprise data policies.
- Establish architecture principles and reusable patterns for development teams.
Technical Skills
- Strong experience in Data Architecture and Integration Architecture
- Data Lake / Lakehouse / Data Warehouse architectures
- Domain-driven data architecture and data products
- Microsoft Fabric / Azure / Databricks or comparable platforms
- SQL and good working knowledge of Python/PySpark
- API and event-driven architectures
- Kafka/Event Hubs or similar streaming technologies
- ETL/ELT and CDC patterns
- Data modeling and canonical models
- Data governance, lineage and metadata management
- Cloud security and access-control concepts
- Exposure to AI/ML and GenAI data architectures
Preferred Manufacturing Experience
- Manufacturing IT / OT landscapes
- MES, ERP, planning, quality or shopfloor systems
- IoT and equipment data
- Manufacturing master/reference data
- Production planning and engineering data
- Digital Twin / Factory Digitalization use cases
Expected Profile
- Approximately 8+ years of relevant experience, depending on the internal leveling model.
- Strong background in enterprise data architecture, integration or data-platform engineering.
- Ability to move between business/domain discussions and detailed technical architecture.
- Should be capable of hands-on prototyping and technical validation, not only architecture documentation.
Job description provided by Mercedes-Benz Group AG.
View the original posting
