Data Engineer (Azure & Databricks)

Role Overview

We are seeking a highly skilled Data Engineer with strong experience in Azure and Databricks, who will play a critical role in designing, transforming, and operationalizing data pipelines within a modern Lakehouse architecture.

The role primarily focuses on transforming data from the Bronze layer into curated analytics-ready datasets, building automated CI/CD pipelines, and developing high-quality Python and PySpark-based data solutions. The engineer will also collaborate closely with Data Scientists and Software Engineers and should be open to contributing to data-driven UI/UX initiatives.

Data Engineering & Transformation

• Design, develop, and maintain scalable data transformation pipelines using Python (with tools like PySpark, ADF) and SQL in Azure Databricks
• Implement transformation logic to move data from Bronze to Silver/Gold layers following data engineering best practices
• Apply strong data engineering principles to ensure data reliability, quality, performance, and reusability
• Work with structured and semi-structured data at scale
  
  

Databricks, Azure & Cloud ETL

• Build and manage Databricks notebooks, jobs, Delta Lake tables, and orchestrated workflows
• Hands-on experience with Cloud-based ETL platforms
  
  

(Preferred: Microsoft Azure Databricks, Synapse, Azure Functions; otherwise AWS or Google Cloud)

• Optimize data pipelines for performance, scalability, and cost efficiency
  
  

Python Applications, APIs & Automation

• Design, develop, and maintain Python applications, scripts, and APIs for data processing and automation
• Write production-grade Python code with strong focus on readability, maintainability, and testing
• Leverage Python for orchestration, validation, and integration with downstream systems
  
  

Collaboration with Data Science & Engineering Teams

• Collaborate closely with Data Scientists and Data Analysts to understand data, analytical models, and consumption requirements
• Enable and support advanced analytics and data science workflows by preparing high-quality feature datasets
• Translate analytical needs into scalable data engineering solutions
  
  

CI/CD, DevOps & Platform Engineering

• Build and maintain automated CI/CD pipelines for data and Databricks workloads
• Hands-on experience with DevOps tools and practices, including Git-based version control
• Exposure to containerization and orchestration platforms such as Kubernetes / OpenShift
• Ensure smooth promotion of code and pipelines across environments (Dev/Test/Prod)
  
  

Data Modeling & Querying

• Design and implement robust data models optimized for analytics and reporting
• Strong hands-on knowledge of SQL and exposure to KQL or other query languages
• Apply best practices in data structures, indexing, and performance tuning UI / UX & Data Applications (Additional Advantage)
• Open to contributing to data-driven UI/UX components, dashboards, or lightweight data applications
• Work with analytics and business teams to improve data usability and customer experience
  
  

Required Skills & Qualifications

Must-Have

• Strong hands-on expertise in Python (with frameworks like PySpark)
• Solid foundation in Data Engineering principles and large-scale data processing
• Experience with Azure Databricks and cloud-based ETL platforms
• Strong knowledge of SQL and data querying techniques
• Experience with CI/CD pipelines and DevOps practices
• Experience in pipeline monitoring and alerting
• Ability to design efficient, scalable solutions to complex data problems
  
  

Good-to-Have

• Experience with Azure Synapse, Azure Functions
• Exposure to AWS or Google Cloud data platforms
• Hands-on experience with OpenShift
• Knowledge of data science concepts and workflows
• Familiarity with analytics platforms, dashboards, and UI/UX considerations