Senior Data Engineer

As a Senior Data Engineer at our company, you will play a crucial role in designing and implementing scalable data pipelines, productionizing ML workflows, and ensuring system observability. This hands-on role requires expertise in Databricks, Spark, AWS, and MLOps. You will be instrumental in contributing from day one to high-impact projects in cutting-edge cloud & AI/ML environments. **Key Responsibilities:** - Build and optimize data pipelines using Databricks and Spark. - Implement MLOps best practices including CI/CD, MLflow/SageMaker, monitoring, and retraining. - Develop and manage cloud-native solutions on AWS. - Design and monitor KPIs for data systems. - Apply data observability practices to ensure reliability and performance. - Collaborate closely with data scientists, DevOps, and business teams. **Must-Have Skills:** - 5+ years of hands-on Data Engineering experience. - Strong proficiency in Python, SQL, and APIs. - Expertise in Databricks and Spark for data transformations & ETL. - Experience with AWS for data engineering & ML services. - Familiarity with MLOps tools like MLflow, SageMaker, or equivalent. - Strong experience in KPI design & implementation for data systems. - Knowledge of data observability platforms. **Nice-to-Have Skills:** - Experience with Airflow for workflow orchestration. - Familiarity with Jenkins for CI/CD. - Knowledge of containerization with Docker and Kubernetes. - Proficiency in Terraform for IaC. - Familiarity with monitoring tools like Grafana / ELK Stack. - Understanding of lakehouse architecture concepts. If you are interested in this role and meet the above criteria, please share your updated CV to career@strive4x.net with the subject line: Application Senior Data Engineer Contract [Your Name] [Notice Period]. Kindly include the following details in your email: Full Name, Total Experience, Relevant Experience in Databricks, Spark, AWS, MLOps, Current Company & Location, Current CTC & Expected CTC (Contract), Notice Period (only 20 days accepted), and Preferred Work Mode (Hybrid Location / Remote). As a Senior Data Engineer at our company, you will play a crucial role in designing and implementing scalable data pipelines, productionizing ML workflows, and ensuring system observability. This hands-on role requires expertise in Databricks, Spark, AWS, and MLOps. You will be instrumental in contributing from day one to high-impact projects in cutting-edge cloud & AI/ML environments. **Key Responsibilities:** - Build and optimize data pipelines using Databricks and Spark. - Implement MLOps best practices including CI/CD, MLflow/SageMaker, monitoring, and retraining. - Develop and manage cloud-native solutions on AWS. - Design and monitor KPIs for data systems. - Apply data observability practices to ensure reliability and performance. - Collaborate closely with data scientists, DevOps, and business teams. **Must-Have Skills:** - 5+ years of hands-on Data Engineering experience. - Strong proficiency in Python, SQL, and APIs. - Expertise in Databricks and Spark for data transformations & ETL. - Experience with AWS for data engineering & ML services. - Familiarity with MLOps tools like MLflow, SageMaker, or equivalent. - Strong experience in KPI design & implementation for data systems. - Knowledge of data observability platforms. **Nice-to-Have Skills:** - Experience with Airflow for workflow orchestration. - Familiarity with Jenkins for CI/CD. - Knowledge of containerization with Docker and Kubernetes. - Proficiency in Terraform for IaC. - Familiarity with monitoring tools like Grafana / ELK Stack. - Understanding of lakehouse architecture concepts. If you are interested in this role and meet the above criteria, please share your updated CV to career@strive4x.net with the subject line: Application Senior Data Engineer Contract [Your Name] [Notice Period]. Kindly include the following details in your email: Full Name, Total Experience, Relevant Experience in Databricks, Spark, AWS, MLOps, Current Company & Location, Current CTC & Expected CTC (Contract), Notice Period (only 20 days accepted), and Preferred Work Mode (Hybrid Location / Remote).