Data Engineer

Salary undisclosed

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Key Responsibilities:

Data Pipeline Development: Design and implement efficient ETL (Extract, Transform, Load) processes to collect data from various sources, transforming it into a usable format for analysis.
Data Warehouse Management: Build and manage data warehousing solutions, ensuring optimal storage and retrieval of data across various platforms.
Data Architecture: Collaborate with stakeholders to define data architecture and ensure alignment with business objectives.
Performance Optimization: Monitor and optimize data systems and pipelines for performance, scalability, and reliability.
Data Quality Assurance: Implement data quality checks and monitoring processes to ensure accuracy and consistency in datasets.
Collaboration: Work with data analysts, data scientists, and other stakeholders to understand data requirements and deliver on data needs effectively.
Technical Documentation: Document data flow processes, architectures, and other important workflows for reference and to facilitate onboarding of new team members.

Requirements:

Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field.
Proven experience as a Data Engineer or in a similar role, with strong knowledge of data engineering practices.
Proficiency in programming languages such as Python, Java, or Scala, with experience in data manipulation libraries.
Strong SQL skills for querying and managing relational databases.
Familiarity with big data technologies and data warehousing solutions (e.g., Snowflake, Amazon Redshift).
Experience with cloud technologies (AWS, Azure, or Google Cloud) and their data services.
Knowledge of data visualization tools (e.g., Tableau, Power BI) to understand and communicate data insights effectively.
Understanding of machine learning concepts and data modeling techniques is a plus.
Experience with version control systems (e.g., Git) and agile methodologies.

Please send your detailed resume in MS Word format to [email protected] with

Key Responsibilities:

Data Pipeline Development: Design and implement efficient ETL (Extract, Transform, Load) processes to collect data from various sources, transforming it into a usable format for analysis.
Data Warehouse Management: Build and manage data warehousing solutions, ensuring optimal storage and retrieval of data across various platforms.
Data Architecture: Collaborate with stakeholders to define data architecture and ensure alignment with business objectives.
Performance Optimization: Monitor and optimize data systems and pipelines for performance, scalability, and reliability.
Data Quality Assurance: Implement data quality checks and monitoring processes to ensure accuracy and consistency in datasets.
Collaboration: Work with data analysts, data scientists, and other stakeholders to understand data requirements and deliver on data needs effectively.
Technical Documentation: Document data flow processes, architectures, and other important workflows for reference and to facilitate onboarding of new team members.

Requirements:

Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field.
Proven experience as a Data Engineer or in a similar role, with strong knowledge of data engineering practices.
Proficiency in programming languages such as Python, Java, or Scala, with experience in data manipulation libraries.
Strong SQL skills for querying and managing relational databases.
Familiarity with big data technologies and data warehousing solutions (e.g., Snowflake, Amazon Redshift).
Experience with cloud technologies (AWS, Azure, or Google Cloud) and their data services.
Knowledge of data visualization tools (e.g., Tableau, Power BI) to understand and communicate data insights effectively.
Understanding of machine learning concepts and data modeling techniques is a plus.
Experience with version control systems (e.g., Git) and agile methodologies.

Please send your detailed resume in MS Word format to [email protected] with