Dataops Engineer

Job Details - DataOps / DevOps Engineer Azure | CI/CD | Data Platform | Observability DataOps | DevOps | Airflow/ADF | Spark/Kafka | Snowflake | Python/SQL | MLOps Role Overview We are looking for a highly motivated DataOps / DevOps Engineer to build and scale reliable, automated, and efficient deployment pipelines across our modern data platform. This role focuses on enabling seamless collaboration between Data Engineering, Analytics, and AI/ML teams by implementing strong DevOps and DataOps practices ensuring platform reliability, observability, governance, and operational excellence. You will be responsible for implementing CI/CD workflows, deployment automation, monitoring, infrastructure reliability, and operational support for batch and real-time data ecosystems in a cloud-based Azure environment.

This role plays a critical part in supporting analytics, AI/ML, and enterprise-scale data initiatives across the organization. Key Responsibilities ▪ Implement and manage CI/CD pipelines for data workflows, including automated testing, deployment, rollback, and version control. ▪ Build and scale reliable, automated, and efficient deployment pipelines for data and AI/ML platforms. ▪ Support deployment and operationalization of batch and real-time data systems in collaboration with Data Engineering teams. ▪ Manage and optimize orchestration and workflow tools such as Airflow, Azure Data Factory, or equivalent platforms. ▪ Ensure platform reliability through robust monitoring, logging, alerting, and observability frameworks. ▪ Define and manage SLAs/SLOs, ensuring uptime, stability, and operational performance of data platforms. ▪ Optimize cloud infrastructure and workloads for performance, scalability, and cost efficiency. ▪ Support distributed systems and large-scale processing platforms using technologies such as Spark, Kafka, and Snowflake. ▪ Collaborate with Data Engineers, ML Engineers, Data Scientists, and Analysts to enable production-ready data and AI solutions. ▪ Support deployment and operational management of AI/ML Ops pipelines and model lifecycle workflows. Required Skills & Experience ▪ 2–3 years of experience in DataOps, DevOps, Platform Engineering, or Cloud Engineering roles. ▪ Strong understanding of CI/CD concepts, deployment automation, and DevOps best practices. ▪ Hands-on experience with Azure cloud platform (Azure DevOps, Data Factory, Databricks, Data Lake, etc.) or equivalent cloud ecosystems. ▪ Experience with CI/CD tools such as Azure DevOps, Jenkins, or GitHub Actions. ▪ Familiarity with big data and distributed systems such as Spark, Kafka, and Snowflake. ▪ Strong proficiency in SQL, Python/Shell scripting, and Linux system operations. ▪ Experience with monitoring and observability tools such as Prometheus, Grafana, or equivalent. ▪ Strong troubleshooting, debugging, and operational support skills.

Good to Have ▪ Experience with Airflow/Dagster and workflow orchestration platforms. ▪ Exposure to AI/ML Ops pipelines, model deployment workflows, and feature engineering support. ▪ Knowledge of Infrastructure as Code (IaC) using Terraform, ARM templates, or Azure Bicep. ▪ Understanding of Delta Lake / Lakehouse architecture. ▪ Familiarity with data quality, governance, lineage, and cataloging frameworks. ▪ Exposure to Kubernetes, containerization, and cloud-native deployment practices.