Senior Data Engineer

Zoho; Ahmedabad District; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Zoho in Ahmedabad, India is seeking a data engineer to design, develop, and maintain scalable batch and real-time data pipelines, building ETL/ELT workflows and managing data lakehouse architectures.

You will optimize data processing for performance and cost, implement data quality checks, and collaborate with analytics, BI, and AI teams to deliver trusted datasets on cloud platforms (AWS/Azure/GCP). Must have strong Python, PySpark, SQL and experience with Airflow or similar.

Qualifications

• 4 to 5 years of experience in Data Engineering.
• Strong expertise in Python, PySpark, and SQL.
• Hands-on experience with Spark-based data processing at production scale.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Strong understanding of Data Lakes, Data Warehouses, and Lakehouse architectures.
• Experience with orchestration tools such as Airflow, Azure Data Factory, or similar.
• Experience with data modeling, performance tuning, and pipeline optimization.
• Knowledge of CI/CD, Git, and DevOps practices.
• Strong communication and stakeholder management skills.

Responsibilities

• Design, develop, and maintain scalable batch and real-time data pipelines.
• Build and optimize ETL/ELT workflows using modern data engineering frameworks.
• Develop and manage data lake, data warehouse, lake house architecture and snowflakes.
• Implement data quality checks, validation frameworks, and monitoring solutions.
• Optimize data processing jobs for performance, scalability, and cost efficiency.
• Collaborate with analytics, BI, and AI teams to deliver trusted datasets.
• Build and maintain cloud-native data platforms on AWS, Azure, or GCP.
• Implement Infrastructure as Code (IaC) using Terraform or equivalent tools.
• Ensure security, compliance, and governance standards across data platforms.
• Work directly with stakeholders to understand business requirements and translate them into technical solutions.
• Participate in architecture discussions, design reviews, and sprint planning.
• Perform code reviews and mentor junior engineers.
• Contribute to technical documentation, standards, and reusable assets.
• Evaluate and recommend modern data engineering tools and frameworks.
• Contribute to internal accelerators, reusable components, and best practices.
• Support proof-of-concepts and emerging technology initiatives in analytics, AI, and data platforms.

Skills

Python

PySpark

SQL

Spark

Cloud platforms

Data Lakes/Warehouses

Lakehouse

Airflow

Azure Data Factory

Git

CI/CD

DevOps

Data Modeling

Performance Tuning

Stakeholder Management

Tools

Airflow

Azure Data Factory

Git

Job description

• Design,develop, and maintain scalable batch and real-time data pipelines.
  


• Build andoptimize ETL/ELT workflows using modern data engineering frameworks.
  


• Developand manage data lake, data warehouse, lake house architecture and snowflakes.
  


• Implementdata quality checks, validation frameworks, and monitoring solutions.
  


• Optimizedata processing jobs for performance, scalability, and cost efficiency.
  


• Collaboratewith analytics, BI, and AI teams to deliver trusted datasets.
  


• Build andmaintain cloud-native data platforms on AWS, Azure, or GCP.
  


• ImplementInfrastructure as Code (IaC) using Terraform or equivalent tools.
  


• Ensuresecurity, compliance, and governance standards across data platforms.
  


• Workdirectly with stakeholders to understand business requirements and translatethem into technical solutions.
  


• Participatein architecture discussions, design reviews, and sprint planning.
  


• Performcode reviews and mentor junior engineers.
  


• Contributeto technical documentation, standards, and reusable assets.
  


• Evaluateand recommend modern data engineering tools and frameworks.
  


• Contributeto internal accelerators, reusable components, and best practices.
  


• Supportproof-of-concepts and emerging technology initiatives in analytics, AI, anddata platforms.
  

Requirements

Must have

• 4 to 5years of experience in Data Engineering.
  


• Strongexpertise in Python, PySpark, and SQL.
  


• Hands-onexperience with Spark-based data processing at production scale.
  


• Experiencewith cloud platforms such as AWS, Azure, or GCP.
  


• Strongunderstanding of Data Lakes, Data Warehouses, and Lakehouse architectures.
  


• Experiencewith orchestration tools such as Airflow, Azure Data Factory, or similar.
  


• Experiencewith data modeling, performance tuning, and pipeline optimization.
  


• Knowledgeof CI/CD, Git, and DevOps practices.
  


• Strongcommunication and stakeholder management skills
  

MandatoryCertifications (Any One Required)

• Google ProfessionalData Engine

• Opportunity to work on enterprise-scale cloudand data engineering projects.


• Exposure to global clients and modern dataplatforms.


• Sponsorship for advanced cloud and dataengineering certifications.


• Clear career progression toward Lead DataEngineer and Architect roles.


• Collaborative engineering culture focused onlearning and innovation.


• Competitivecompensation with performance-based incentives.