Deliver production-ready datasets and pipelines to support data science and analytics.
Bridge the gap between ML experimentation and deployment with MLOps best practices.
Build and deploy data-driven applications using Azure services.
Maximize ROI on Azure investment through cloud-native architecture.
Standardize development workflows using GitHub (version control, pull requests, CI/CD).
Automate deployments and accelerate engineering velocity with GitHub Actions.
Reduce bottlenecks for analysts, engineers, and data scientists by improving data accessibility and workflow efficiency.
Enhance data quality, governance, and observability.
Enable future capabilities such as AI, automation, and personalization.

5+ years of experience in data engineering or related roles.
Proven experience in data engineering, cloud architecture, and MLOps.
Strong proficiency in Python and SQL for data pipelines, automation, and data framework (Spark, Panda).
Familiarity with REST APIs and application development concepts.
Strong knowledge of Azure services (Data Factory, Synapse, Databricks, etc.) and cloud-native solutions.
Proficiency with GitHub, including version control, CI/CD, and automation using GitHub Actions, workflows, and PR reviews.
Experience delivering production-ready datasets and pipelines for analytics and ML.
Ability to collaborate effectively with data scientists, analysts, and engineers to prepare and deliver clean, feature-ready datasets.
Knowledge with data governance, observability, and quality best practices is preferred.
Experience with real-time streaming tools (Kafka, Event Hub) also preferred.
Strong problem-solving skills and a passion for building scalable, maintainable, and automated data platforms.
Experience with Microsoft Fabric of Lakehouse architectures is a nice-to-have.

.Kreate

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