Data Engineer

Data Engineer

We are looking for an experienced Data Engineer to design, develop, implement, and maintain scalable data pipelines and data integration solutions. The ideal candidate will have strong experience in SQL, Python, ETL/ELT processes, data warehousing, cloud platforms, and big data technologies. The Data Engineer will work closely with data architects, data analysts, data scientists, and business stakeholders to build reliable, high-performance data solutions that support enterprise reporting, analytics, and business intelligence.

Key Responsibilities

The Data Engineer will:

• Design, develop, and maintain scalable data pipelines and ETL/ELT processes.
• Build and optimize data integration workflows to extract, transform, and load data from multiple sources.
• Develop complex SQL queries, stored procedures, and database optimization strategies.
• Use Python, PySpark, or Scala for data processing and transformation.
• Design and maintain data warehouses, data lakes, and lakehouse architectures.
• Implement data solutions using cloud platforms such as AWS, Azure, or Google Cloud Platform (Google Cloud Platform).
• Work with big data technologies such as Apache Spark, Hadoop, Kafka, and Databricks.
• Develop and maintain data models, schemas, and data architecture.
• Ensure data quality, accuracy, consistency, security, and governance.
• Monitor, troubleshoot, and optimize data pipelines for performance and reliability.
• Collaborate with cross-functional teams to understand business requirements and deliver data solutions.
• Implement CI/CD pipelines and automated deployment processes for data engineering solutions.
• Maintain technical documentation and follow data engineering best practices.

Requirements

• 7+ years of experience in Data Engineering or a related role.
• Strong proficiency in SQL and Python.
• Hands-on experience with ETL/ELT tools and processes.

• Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform (Google Cloud Platform).
• Familiarity with big data technologies such as Apache Spark, Hadoop, Kafka, and Databricks.
• Knowledge of data warehousing solutions and data lake architectures.
• Understanding of data quality, accuracy, consistency, security, and governance.
• Experience with CI/CD pipelines and automated deployment processes.
• Strong problem-solving skills and attention to detail.
• Excellent communication and collaboration skills.