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

%AA; Implement and manage CI/CD pipelines for data workflows, including automated testing, deployment, rollback, and version control.

%AA; Build and scale reliable, automated, and efficient deployment pipelines for data and AI/ML platforms.

%AA; Support deployment and operationalization of batch and real-time data systems in collaboration with Data Engineering teams.

%AA; Manage and optimize orchestration and workflow tools such as Airflow, Azure Data Factory, or equivalent platforms.

%AA; Ensure platform reliability through robust monitoring, logging, alerting, and observability frameworks.

%AA; Define and manage SLAs/SLOs, ensuring uptime, stability, and operational performance of data platforms.

%AA; Optimize cloud infrastructure and workloads for performance, scalability, and cost efficiency.

%AA; Support distributed systems and large-scale processing platforms using technologies such as Spark, Kafka, and Snowflake.

%AA; Collaborate with Data Engineers, ML Engineers, Data Scientists, and Analysts to enable production-ready data and AI solutions.

%AA; Support deployment and operational management of AI/ML Ops pipelines and model lifecycle workflows.

Required Skills & Experience

%AA; 2–3 years of experience in DataOps, DevOps, Platform Engineering, or Cloud Engineering roles.

%AA; Strong understanding of CI/CD concepts, deployment automation, and DevOps best practices.

%AA; Hands-on experience with Azure cloud platform (Azure DevOps, Data Factory, Databricks, Data Lake, etc.) or equivalent cloud ecosystems.

%AA; Experience with CI/CD tools such as Azure DevOps, Jenkins, or GitHub Actions.

%AA; Familiarity with big data and distributed systems such as Spark, Kafka, and Snowflake.

%AA; Strong proficiency in SQL, Python/Shell scripting, and Linux system operations.

%AA; Experience with monitoring and observability tools such as Prometheus, Grafana, or equivalent.

%AA; Strong troubleshooting, debugging, and operational support skills.

Good to Have

%AA; Experience with Airflow/Dagster and workflow orchestration platforms.

%AA; Exposure to AI/ML Ops pipelines, model deployment workflows, and feature engineering support.

%AA; Knowledge of Infrastructure as Code (IaC) using Terraform, ARM templates, or Azure Bicep.

%AA; Understanding of Delta Lake / Lakehouse architecture.

%AA; Familiarity with data quality, governance, lineage, and cataloging frameworks.

%AA; Exposure to Kubernetes, containerization, and cloud-native deployment practices.