Role Overview

We are looking for an experienced Data Engineer with strong hands-on expertise in Azure Databricks, PySpark, Python, dbt, and Snowflake. The ideal candidate should have a strong background in building scalable data pipelines, data transformation workflows, and enterprise-grade data engineering solutions.

Mandatory Skills

Candidates must have strong hands-on experience with:

- Azure Databricks – Mandatory
- PySpark – Mandatory
- Python – Mandatory
- dbt – Mandatory
- Snowflake – Mandatory
- Strong SQL and Data Warehousing knowledge

Key Responsibilities

- Design, develop, and maintain scalable data engineering solutions using Azure Databricks and PySpark.
- Develop robust and reusable data pipelines using Python and PySpark.
- Build and optimize ETL/ELT pipelines for large-scale data processing.
- Develop and maintain data transformation workflows using dbt.
- Work extensively with Snowflake for data storage, transformation, optimization, and analytics workloads.
- Integrate data from multiple source systems into Azure and Snowflake platforms.
- Develop complex SQL queries, stored procedures, transformations, and optimized data models.
- Implement data quality, validation, reconciliation, and error-handling mechanisms.
- Optimize Databricks jobs, Spark workloads, PySpark code, and Snowflake queries for performance and cost.
- Design scalable batch and, where required, near-real-time data pipelines.
- Troubleshoot production pipeline failures and resolve technical issues.
- Follow coding standards, CI/CD practices, version control, and deployment best practices.
- Collaborate with architects, business stakeholders, and engineering teams to deliver reliable data solutions.

Technical RequirementsAzure Databricks

- Strong hands-on experience with Azure Databricks.




- Experience developing and deploying Databricks notebooks and jobs.
- Strong understanding of Spark architecture and distributed data processing.
- Experience with performance tuning and optimization.

PySpark

- Strong hands-on experience developing PySpark applications.
- Excellent understanding of DataFrames, transformations, actions, joins, partitioning, caching, and optimization.
- Experience handling large-volume datasets.

Python

- Strong programming skills in Python.
- Experience developing reusable, production-grade Python code.
- Strong understanding of exception handling, modular programming, and debugging.

dbt

- Hands-on experience with dbt development.
- Experience building data transformation models, tests, documentation, and dependencies.
- Strong understanding of ELT and modern data transformation frameworks.

Snowflake

- Strong hands-on experience with Snowflake.
- Experience with data loading, transformation, warehouse optimization, and performance tuning.
- Strong SQL skills.
- Experience designing scalable Snowflake data models.

Additional Requirements

- Strong understanding of Data Warehousing and ETL/ELT concepts.
- Strong SQL and data modeling skills.
- Experience working with large-scale enterprise data platforms.
- Good understanding of data quality and data governance practices.
- Experience working in Agile/Scrum environments.
- Strong analytical, troubleshooting, and problem-solving skills.
- Excellent communication and collaboration skills.

Candidate Profile

The ideal candidate should be a hands-on Data Engineer capable of independently designing, developing, optimizing, and troubleshooting enterprise-scale data pipelines. Strong practical experience across Azure Databricks + PySpark + Python + dbt + Snowflake is essential.

Pay: ₹90,000.00 - ₹120,000.00 per month

Advantages:

- Provident Fund

Work Location: Remote

📌 Data Engineer(Exp:6-8yrs) (India)
🏢 ZecData Technology
📍 India