Data Engineer

Key Responsibilities:

Design and implement scalable, metadata-driven frameworks for data ingestion, quality, and transformation across both batch

and streaming datasets.

Develop and optimize end-to-end data pipelines to process structured and unstructured data, enabling the creation of analytical

data products.

• Build robust exception handling, logging, and monitoring mechanisms for better observability and operational support.
• Take ownership of complex modules and lead the development of critical data workflows and components.
• Provide guidance to data engineers and peers on best practices.

Collaborate with cross-functional teamsincluding business consultants, data architects, scientists, and application

developers—to deliver impactful analytics solutions.

EXPERTISE AND QUALIFICATIONS

3+ years of overall technical experience, with a minimum of 2 years of hands-on experience with Microsoft Azure and

Databricks.

Proven experience delivering at least one end-to-end Data Lakehouse solution on Azure Databricks using the Medallion

Architecture.

Strong working knowledge of the Databricks ecosystem, including: PySpark, Notebooks, Structured Streaming, Unity Catalog,

Delta Live Tables, Workflows, and SQL Warehouse.

• Advanced programming, unit testing, and debugging skills in Python and SQL.

Hands-on experience with Azure-native services such as: Azure Data Factory, ADLS Gen2, Azure SQL Database, and Event

Hub.

• Solid understanding of data modeling techniques, including both Dimensional and Third Normal Form (3NF) models.
• Exposure to developing LLM/Generative AI-powered applications.
• Must have excellent understanding of CI/CD workflows using Azure DevOps.
• Bonus: Knowledge of Azure infrastructure, including provisioning, networking, security, and governance.