Original listing text, shown exactly as published by the company.
Key Responsibilities
- Collaborate with cross-functional teams to design, build, maintain, and monitor high-quality Data Products that meet diverse customer and business requirements.
- Act as a subject matter expert (SME) by bridging technical and business perspectives to deliver meaningful, data-driven solutions.
- Manage the end-to-end data lifecycle, including data ingestion, transformation, storage, and provisioning for downstream consumers.
- Apply software engineering best practices—CI/CD, Git-based version control, Test-Driven Development (TDD), Definition of Done (DoD), and Agile methodologies—to data engineering workflows.
- Ensure data security, governance, and compliance standards are met across all data products.
- Develop and maintain clear documentation, promote knowledge sharing, and support onboarding for data products and platforms.
- Monitor data pipelines and products for reliability, performance, scalability, and cost efficiency.
- Integrate data products into broader digital and application ecosystems, supporting both real-time and batch use cases.
- Actively contribute to continuous improvement of data architecture, tooling, and processes.
Technical Skillset
Data Engineering & Analytics
- Data pipelines, ETL / ELT processes
- Data modeling and analytics
- Data exploration and visualization
Cloud Platforms
- AWS (strong hands-on experience):
- S3, Redshift, Athena, Lambda, Glue
- CodePipeline, CloudFormation
- SNS
- Working knowledge of: Microsoft Azure and Google Cloud Platform (GCP)
Data Tools & Frameworks
- SSIS
- Databricks
- Spark
- SQL
- Python
- R, Scala (good to have)
Databases
- SQL Server
- DynamoDB
- ArangoDB
Data Formats
Visualization & Exploration Tools
- Amazon QuickSight
- Tableau
- Microsoft Excel
Collaboration & Documentation
Core Competencies
- Strong product mindset with a customer-centric approach to data solutions
- Excellent planning, communication, and organizational skills
- Proficiency with Git / GitHub and version control best practices
- Experience with CLI scripting (Bash, PowerShell, AWS CLI)
- Agile mindset with exposure to Test-Driven Development (TDD)
- Familiarity with modern data management concepts, including Data Mesh and Event-Driven / Streaming architectures
t
Specialization
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Job Title: Data Product Engineer
Experience: 5+ Years
Location: India (Hybrid / Remote as applicable)
Job Summary
We are looking for an experienced Data Product Engineer with a strong background in building, delivering, and supporting data-driven products for both operational and analytical use cases. The ideal candidate will combine product thinking with deep data engineering expertise to transform raw data into scalable, secure, and production-ready data products that integrate seamlessly into modern digital ecosystems.
This role requires close collaboration with business stakeholders, strong software engineering practices, and a solid understanding of cloud-native data platforms.
Key Responsibilities
- Collaborate with cross-functional teams to design, build, maintain, and monitor high-quality Data Products that meet diverse customer and business requirements.
- Act as a subject matter expert (SME) by bridging technical and business perspectives to deliver meaningful, data-driven solutions.
- Manage the end-to-end data lifecycle, including data ingestion, transformation, storage, and provisioning for downstream consumers.
- Apply software engineering best practices—CI/CD, Git-based version control, Test-Driven Development (TDD), Definition of Done (DoD), and Agile methodologies—to data engineering workflows.
- Ensure data security, governance, and compliance standards are met across all data products.
- Develop and maintain clear documentation, promote knowledge sharing, and support onboarding for data products and platforms.
- Monitor data pipelines and products for reliability, performance, scalability, and cost efficiency.
- Integrate data products into broader digital and application ecosystems, supporting both real-time and batch use cases.
- Actively contribute to continuous improvement of data architecture, tooling, and processes.
Technical Skillset
Data Engineering & Analytics
- Data pipelines, ETL / ELT processes
- Data modeling and analytics
- Data exploration and visualization
Cloud Platforms
- AWS (strong hands-on experience):
- S3, Redshift, Athena, Lambda, Glue
- CodePipeline, CloudFormation
- SNS
- Working knowledge of: Microsoft Azure and Google Cloud Platform (GCP)
Data Tools & Frameworks
- SSIS
- Databricks
- Spark
- SQL
- Python
- R, Scala (good to have)
Databases
- SQL Server
- DynamoDB
- ArangoDB
Data Formats
Visualization & Exploration Tools
- Amazon QuickSight
- Tableau
- Microsoft Excel
Collaboration & Documentation
Core Competencies
- Strong product mindset with a customer-centric approach to data solutions
- Excellent planning, communication, and organizational skills
- Proficiency with Git / GitHub and version control best practices
- Experience with CLI scripting (Bash, PowerShell, AWS CLI)
- Agile mindset with exposure to Test-Driven Development (TDD)
- Familiarity with modern data management concepts, including Data Mesh and Event-Driven / Streaming architectures
Job requirements
Job Title: Data Product Engineer
Experience: 5+ Years
Location: India (Hybrid / Remote as applicable)
Job Summary
We are looking for an experienced Data Product Engineer with a strong background in building, delivering, and supporting data-driven products for both operational and analytical use cases. The ideal candidate will combine product thinking with deep data engineering expertise to transform raw data into scalable, secure, and production-ready data products that integrate seamlessly into modern digital ecosystems.
This role requires close collaboration with business stakeholders, strong software engineering practices, and a solid understanding of cloud-native data platforms.
Key Responsibilities
- Collaborate with cross-functional teams to design, build, maintain, and monitor high-quality Data Products that meet diverse customer and business requirements.
- Act as a subject matter expert (SME) by bridging technical and business perspectives to deliver meaningful, data-driven solutions.
- Manage the end-to-end data lifecycle, including data ingestion, transformation, storage, and provisioning for downstream consumers.
- Apply software engineering best practices—CI/CD, Git-based version control, Test-Driven Development (TDD), Definition of Done (DoD), and Agile methodologies—to data engineering workflows.
- Ensure data security, governance, and compliance standards are met across all data products.
- Develop and maintain clear documentation, promote knowledge sharing, and support onboarding for data products and platforms.
- Monitor data pipelines and products for reliability, performance, scalability, and cost efficiency.
- Integrate data products into broader digital and application ecosystems, supporting both real-time and batch use cases.
- Actively contribute to continuous improvement of data architecture, tooling, and processes.
Technical Skillset
Data Engineering & Analytics
- Data pipelines, ETL / ELT processes
- Data modeling and analytics
- Data exploration and visualization
Cloud Platforms
- AWS (strong hands-on experience):
- S3, Redshift, Athena, Lambda, Glue
- CodePipeline, CloudFormation
- SNS
- Working knowledge of: Microsoft Azure and Google Cloud Platform (GCP)
Data Tools & Frameworks
- SSIS
- Databricks
- Spark
- SQL
- Python
- R, Scala (good to have)
Databases
- SQL Server
- DynamoDB
- ArangoDB
Data Formats
Visualization & Exploration Tools
- Amazon QuickSight
- Tableau
- Microsoft Excel
Collaboration & Documentation
Core Competencies
- Strong product mindset with a customer-centric approach to data solutions
- Excellent planning, communication, and organizational skills
- Proficiency with Git / GitHub and version control best practices
- Experience with CLI scripting (Bash, PowerShell, AWS CLI)…
