About the Role
As a Data Engineer (Junior Data Architect), you will play a critical role in shaping and scaling our modern data platform on Azure and Databricks.
This role goes beyond building pipelines. You will support the Data Architect team design reliable, scalable data foundations that power analytics, reporting, and advanced use cases across the business. You will work closely with data, business, and global teams to turn raw data into trusted, high-quality data products.
What You Will Do
1. Build and Scale Data Platforms
Design and develop robust data pipelines from multiple source systems into the enterprise data lake.
Implement scalable data models aligned with the Global Bronze, Silver, Gold architecture.
Optimize data processing for performance, cost, and reliability.
2. End-to-End Data Pipeline Engineering
Develop and orchestrate pipelines using ADF and Databricks.
Manage scheduling, dependencies, and parameterization across workflows.
Ensure production-grade reliability with monitoring, alerting, and incident response.
3. Drive Data Quality & Trust
Define and implement automated data quality checks across domains.
Partner with business and data teams to ensure data consistency and usability.
Continuously improve data observability and reliability.
4. Enable CI/CD and Engineering Best Practices
Own and enhance CI/CD pipelines for data engineering deployments.
Promote version control, testing, and automation across environments (dev, staging, prod).
Contribute to engineering standards and reusable frameworks.
5. Collaborate Across Functions
Work closely with BI, analytics, and master data teams to deliver end-to-end solutions.
Translate business needs into scalable data solutions.
Align with Global teams on architecture standards and roadmap.
6. Support Data Platform Evolution
Contribute to cloud migration and modernization initiatives.
Continuously evaluate and improve platform architecture.
Maintain clear, up-to-date technical documentation.
Experience & Profiles
Education: Bachelor's degree in Engineering, Systems Design, Computer Sciences, or a related field.
Tech stacks: 1-2 years of experience in Azure (ADF, Synapse, Azure DevOps), Databricks (Delta Lake, Spark SQL, notebook orchestration).
Data Modeling: Basic understanding of data warehouse concepts (dimensional modeling, data marts).
Engineering Skills: Experience building scalable and maintainable data pipelines.
DevOps Practice: Basic knowledge of CI/CD practices (Azure DevOps or similar).
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