Senior Data Engineer

What you will do

Data Engineering & Platform Development

• 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.
  

Cloud & Infrastructure

• Build and maintain cloud-native data platforms on AWS, Azure, or GCP.
  
• Implement Infrastructure as Code (IaC) using Terraform or equivalent tools.
  
• Support CI/CD implementation for data engineering workloads.
  
• Ensure security, compliance, and governance standards across data platforms.
  

Collaboration & Delivery

• 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.
  

Innovation & Practice Building

• 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.
  

Requirements

Must have

• 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
  

Mandatory Certifications (Any One Required)

• Databricks Certified Data Engineer Associate
  
• Databricks Certified Data Engineer Professional
  
• AWS Certified Data Engineer – Associate
  
• Microsoft Certified: Azure Data Engineer Associate (DP-203)
  
• Google Professional Data Engine
  

Benefits

● Opportunity to work on enterprise-scale cloud and data engineering projects.

● Exposure to global clients and modern data platforms.

● Sponsorship for advanced cloud and data engineering certifications.

● Clear career progression toward Lead Data Engineer and Architect roles.

● Collaborative engineering culture focused on learning and innovation.

● Competitive compensation with performance-based incentives.