Databricks Engineer
1 month ago
LTIMindtree is a global technology consulting and services company.
We are seeking a highly skilled Databricks Engineer with expertise in Python and Time Series processing.
The ideal candidate will design, construct, install, test, and maintain highly scalable data management systems using Azure Databricks.
The salary for this position is estimated to be around $120,000 - $180,000 per year, depending on location and experience.
The successful candidate will have a strong background in developing data processing pipelines using Databricks Notebooks, Spark SQL, and other Databricks utilities.
They will also have the ability to integrate Azure Databricks with other Azure services such as Azure Data Lake Storage, Azure SQL Data Warehouse, and Azure Cosmos DB.
Key responsibilities include ensuring optimal performance of data processing and extraction, transformation, and loading (ETL) processes.
The engineer will work closely with data architects to implement effective data models and schemas within the Databricks environment.
They will also be responsible for monitoring data reliability, efficiency, and quality, and implementing improvements as needed.
The engineer will collaborate with stakeholders to assist with data-related technical issues and support their data infrastructure needs.
Additional responsibilities include developing and maintaining documentation related to data pipeline architecture, development processes, and data governance.
Designing and implementing high availability and low latency applications, data protection, and security features is also an important part of this role.
The engineer will work on performance tuning and automation of applications, testing and debugging software applications with Python test framework tools like Behave, Pytest, and PyUnit.
Enhancing the functionalities of current software systems using time series analysis and coming up with digital tools for online traffic monitoring are also key responsibilities.
The engineer will work with Python libraries like Pandas and NumPy, creating predictive models for AI and ML-based features.
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