Description - ExternalJob 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.

 

Description - ExternalJob 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.