Data Engineer

About the Company:

Techwave Technologies is a technology consulting company focused on helping organizations build and optimize modern technology solutions. We work with clients across data, cloud, analytics, software development, and emerging technologies to deliver scalable and business-focused solutions.

About the Role:

Position OverviewWe are looking for a skilled Data Engineer to design, develop, and maintain scalable data pipelines and data platforms. The ideal candidate will have hands-on experience with Python, SQL, ETL/ELT, cloud platforms, data warehousing, and data integration.The Data Engineer will work closely with Data Analysts, Business Analysts, Data Scientists, and other technology teams to ensure reliable, secure, and high-quality data is available for reporting, analytics, and business decision-making.

Responsibilities:

• Design, develop, and maintain scalable ETL/ELT data pipelines for structured and unstructured data.
• Develop efficient data processing solutions using Python, SQL, and PySpark.
• Build and optimize data pipelines using tools such as Apache Airflow, Azure Data Factory, AWS Glue, or similar technologies.
• Extract, transform, and load data from databases, APIs, cloud storage, applications, and third-party systems.
• Design and maintain data models, data warehouses, data lakes, and data marts.
• Work with cloud platforms including AWS, Microsoft Azure, or Google Cloud Platform.
• Develop complex SQL queries, stored procedures, views, and data transformation logic.
• Implement data validation, quality checks, reconciliation, and monitoring processes.
• Optimize pipeline performance, database queries, and data processing workloads.
• Work with technologies such as Snowflake, Databricks, Azure Synapse, Amazon Redshift, or similar cloud data platforms.
• Integrate data from REST APIs, relational databases, files, and enterprise applications.
• Implement data partitioning, indexing, and other performance optimization techniques.
• Support real-time and batch data processing requirements.
• Troubleshoot data pipeline failures, data quality issues, and production incidents.
• Implement logging, monitoring, error handling, and alerting for data pipelines.
• Collaborate with cross-functional teams to understand business and technical requirements.
• Follow Agile/Scrum, SDLC, data governance, security, and documentation standards.
• Maintain technical documentation for data pipelines, data models, integrations, and processes.