Senior Data Engineer with MLOps

As a Senior Data Engineer at our company, you will be responsible for designing and implementing scalable data pipelines, productionizing ML workflows, and ensuring system observability. This is a hands-on role that requires expertise in Databricks, Spark, AWS, and MLOps. Your coding skills and execution focus will be crucial in contributing from day one. **Key Responsibilities:** - Build and optimize data pipelines using Databricks and Spark. - Implement MLOps best practices such as 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. **Qualifications Required:** - 5+ years of hands-on Data Engineering experience. - Strong proficiency in Python, SQL, and APIs. - Expertise in Databricks (critical skill), Spark (data transformations & ETL), and AWS (data engineering & ML services). - Experience with MLOps tools (MLflow, SageMaker, or equivalent). - Strong experience in KPI design & implementation for data systems. - Familiarity with data observability platforms. **Nice-to-Have Skills:** - Airflow (workflow orchestration). - Jenkins (CI/CD). - Containerization (Docker, Kubernetes). - Terraform (IaC). - Monitoring tools (Grafana / ELK Stack). - Knowledge of lakehouse architecture. If you are interested in this role and meet the above criteria, please share your updated CV with the subject line: Application Senior Data Engineer Contract [Your Name] [Notice Period]. Send applications to career@strive4x.net. Include your 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).,