Data Engineer (Pyspark,Python,SQL,ETL)
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Role Overview
We are seeking a skilled
Data Engineer
with hands-on experience in
Python, PySpark, and SQL
to design, develop, and maintain scalable data pipelines that support enterprise analytics and reporting. The ideal candidate will have strong experience in data transformation (ETL/ELT), data quality, and working with large datasets in modern data platforms. Key Responsibilities Design, develop, and maintain scalable ETL/ELT pipelines using Python, PySpark, and SQL Perform data extraction, transformation, and loading from multiple source systems into enterprise data platforms Develop reusable data transformation logic to support business reporting, analytics, and machine learning initiatives Optimize SQL queries and PySpark jobs to improve performance and processing efficiency Build and maintain data models, staging layers, and curated datasets for downstream consumption Perform data cleansing, validation, reconciliation, and quality checks to ensure data accuracy and consistency Troubleshoot and resolve data pipeline failures, performance bottlenecks, and data-related issues Collaborate with business analysts, data architects, and data scientists to understand data requirements and implement scalable solutions
Requirements Skills required -Python, SQL, Pyspark, ETL Participate in code reviews, testing, deployment, and production support activities Develop and maintain technical documentation, including data mappings, transformation logic, and ETL workflows Ensure compliance with data governance, security, and regulatory standards Monitor scheduled ETL jobs and proactively address operational issues
Data Engineer
with hands-on experience in
Python, PySpark, and SQL
to design, develop, and maintain scalable data pipelines that support enterprise analytics and reporting. The ideal candidate will have strong experience in data transformation (ETL/ELT), data quality, and working with large datasets in modern data platforms. Key Responsibilities Design, develop, and maintain scalable ETL/ELT pipelines using Python, PySpark, and SQL Perform data extraction, transformation, and loading from multiple source systems into enterprise data platforms Develop reusable data transformation logic to support business reporting, analytics, and machine learning initiatives Optimize SQL queries and PySpark jobs to improve performance and processing efficiency Build and maintain data models, staging layers, and curated datasets for downstream consumption Perform data cleansing, validation, reconciliation, and quality checks to ensure data accuracy and consistency Troubleshoot and resolve data pipeline failures, performance bottlenecks, and data-related issues Collaborate with business analysts, data architects, and data scientists to understand data requirements and implement scalable solutions
Requirements Skills required -Python, SQL, Pyspark, ETL Participate in code reviews, testing, deployment, and production support activities Develop and maintain technical documentation, including data mappings, transformation logic, and ETL workflows Ensure compliance with data governance, security, and regulatory standards Monitor scheduled ETL jobs and proactively address operational issues