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.
Robust 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.

📌 Data Engineer Hyderabad
🏢 Tiger Analytics
📍 Hyderabad