Data Engineer

We are seeking a skilled Data Engineer with hands-on experience in building and maintaining scalable data pipelines and analytics solutions. The ideal candidate will be highly proficient in PySpark, Databricks, and Azure, with strong experience in managing large datasets, data warehousing, and modern data stack technologies. Design, build, and maintain ETL/ELT pipelines for efficient data ingestion, transformation, and storage. Collaborate with data analysts, data scientists, and stakeholders to understand data requirements and deliver actionable solutions. Implement and maintain data models and transformation workflows using Databricks. Optimize performance of large-scale distributed data processing jobs using PySpark. Ensure data quality, consistency, and integrity through validation and monitoring. Work with Azure cloud data platforms (e.g., Azure Data Lake, Azure Data Factory, Azure Databricks, Azure Synapse) to manage data storage and pipelines. Troubleshoot and resolve data-related technical issues. Participate in code reviews, documentation, and best practice enforcement for data engineering workflows.

Minimum 2 years of experience in Data Engineering or a related role. Experience writing code in Python language. Proficiency in PySpark for distributed data processing. Expertise in Databricks for data engineering, transformation, and workflow development. Strong knowledge of SQL and relational databases. Familiarity with data warehousing solutions such as Snowflake, BigQuery, or Redshift. Experience with cloud platforms, with strong preference for Azure. Understanding of data pipelines, ETL/ELT processes, and data architecture principles. Knowledge of version control systems such as Git.