This is a remote position.

Screening Checklist:

Proficiency in interpreting data transformation logic written in T-SQL and implementing equivalent processes within Databricks

Ability to design and implement data ingestion pipelines using Azure Data Factory (from source to RAW layer)

Basic knowledge of C# and Sql(atleast read the coding, no need to write)

Experience in collecting and analyzing performance metrics to optimize data ingestion pipelines

Competence in performing performance optimizations for Databricks read/write queries as needed

Strong motivation and the ability to provide guidance to other data engineers within the team

Job Overview

We are currently looking for highly accomplished Senior Data Engineers (10+ years of experience)

with deep expertise in Databricks and PySpark to play a key role in an ongoing, large-scale data

transformation initiative. The ideal candidates will bring extensive hands-on experience, strong

architectural understanding, and the ability to lead and influence data engineering practices across

teams.





Key capabilities and expectations include:

• Extensive experience in analyzing, interpreting, and modernizing complex data

transformation logic written in T-SQL, and implementing optimized, scalable equivalents within

Databricks using PySpark.

• Proven expertise in architecting and implementing end-to-end data ingestion frameworks

using Azure Data Factory, managing data flows from multiple source systems through the RAW

and subsequent data layers, with a solid focus on reliability and scalability.

• Strong experience in defining, capturing, and analyzing performance metrics, enabling

proactive monitoring, bottleneck identification, and continuous optimization of data ingestion

pipelines.

• Demonstrated ability to perform advanced performance tuning and optimization of Databricks

read/write operations, including partitioning strategies, caching, file formats, and query

optimization techniques.

• Capability

📌 Lead Data Engineer (India)
🏢 Awign Expert
📍 India