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

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

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

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

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

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

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

Must-Have

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

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