Data Engineer

Job Description

The Data Engineer is responsible for building, maintaining, and optimizing data infrastructure that supports business reporting, analytics, and decision‑making. The role assists in designing, developing, and implementing data pipelines that ensure reliable data collection, storage, transformation, and processing across various platforms and systems.

Key Responsibilities

Data Pipeline Development

• Develop and maintain data pipelines for ingesting, transforming, and loading data from multiple sources.

• Support the implementation of ETL/ELT processes and data integration workflows.

• Assist in managing data movement across data warehouses, data lakes, and other platforms.

Data Processing & Integration

• Write and maintain SQL queries and scripts for data extraction, transformation, and reporting.

• Ensure data is properly integrated and available for analytics and business intelligence requirements.

• Support data modeling and optimization initiatives.

Data Quality & Monitoring

• Monitor data pipelines to ensure reliable and timely data delivery.

• Troubleshoot and resolve data processing issues and pipeline failures.

• Perform data validation, reconciliation, and quality assurance checks.

• Maintain data accuracy, consistency, and completeness across systems.

Collaboration & Support

• Work closely with data engineers, analysts, data scientists, and business stakeholders to support data requirements.

• Assist in delivering data solutions for reporting, analytics, and operational needs.

• Participate in continuous improvement of data engineering processes and practices.

Documentation & Best Practices

• Document data workflows, datasets, pipelines, and technical processes.

• Follow coding standards, version control practices, and data engineering best practices.

• Support knowledge sharing and maintain technical documentation.

Qualifications

Education

• Bachelor's Degree in Computer Science, Information Technology, Computer Engineering, or a related field.

Experience

• Familiarity with SQL and database querying.

• Exposure to data warehousing concepts and technologies (e.g., Amazon Redshift).

• Basic knowledge of ETL/ELT processes and data integration tools.

• Familiarity with AWS cloud services and data platforms is an advantage.

Required Skills & Knowledge

• SQL Development and Database Querying

• Python or Similar Programming Languages

• Data Pipeline Development and Maintenance

• ETL/ELT Processes and Data Integration

• Data Warehousing Concepts

• Data Quality and Data Validation Techniques

• Troubleshooting and Issue Resolution

• Cloud Data Platforms (AWS, Amazon S3, AWS Glue)

• Data Processing and Automation

• Documentation and Technical Writing

• Analytical and Problem-Solving Skills

• Attention to Detail and Data Accuracy

• Collaboration and Stakeholder Management

• Version Control and Coding Best Practices

• Proficiency in Microsoft Office Applications (Excel, Word, PowerPoint)