Location: Bengaluru
Employment Type: Full-Time
Role PurposeDefine 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.
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
Python
SQL
Azure
Databricks
Kafka
PySpark
Where the posting names them
- Microsoft Fabric / Azure / Databricks or comparable platforms
- SQL and good working knowledge of Python/PySpark
- API and event-driven 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
New Data Leadership postings at the employers we follow are about the same over the last 6 weeks compared with the 6 before.
August 17Last week
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under Data Leadership, by the work the posting describes.
Data leaders run data teams or design how a company's data fits together. Managers hire, set priorities and answer for whether the data work pays off; architects choose the systems and the shape of the data.
Expect years of hands-on data work, experience leading people or projects, and the ability to explain tradeoffs to executives.
Unlike a senior engineer or analyst, most of a data leader's week goes to people, planning and decisions rather than building.
US government figures for computer and information systems managers
Median yearly pay, May 2025
$175,140
Expected change, 2025 to 2035
15.8% more jobsagainst 3.5% for all jobs
The government has no code for data leaders, so these figures are for the closest group, computer and information systems managers, which includes data processing and data operations managers.
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.
Qualifikationen
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.