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.