Data Engineer

Data Engineering Specialist

Pay Range: $55.00hr - $60.00hr

Requirement/Must Have:

• Strong experience with SQL for complex query development, data modeling, and performance tuning.
• Proficiency in Python for data engineering and ETL development.
• Hands-on experience with SSIS (SQL Server Integration Services).
• Experience designing and managing workflows using Apache Airflow.
• Strong knowledge of Apache Spark for large-scale data processing.
• Experience working with Apache Iceberg tables and lakehouse architectures.
• Hands-on experience with Databricks for data engineering and analytics workloads.
• Strong understanding of data warehousing, ETL/ELT concepts, and distributed data systems.
• Experience with data quality, monitoring, and troubleshooting techniques.
• 5+ years of experience in Data Engineering, ETL Development, or Data Platform Engineering.
• Proven experience delivering enterprise-scale data solutions using modern data technologies.

Responsibilities:

• Design, develop, and optimize scalable data pipelines for batch and real-time data processing.
• Build and maintain robust ETL/ELT solutions using Python, SQL, and SSIS.
• Develop and manage workflow orchestration using Apache Airflow.
• Implement data transformation and processing solutions leveraging Apache Spark.
• Design, optimize, and support data lakehouse architectures using Databricks and Apache Iceberg.
• Ensure data quality, integrity, security, and governance across all data platforms.
• Monitor, troubleshoot, and optimize data ingestion and processing workflows.
• Collaborate with business analysts, data scientists, architects, and application teams to understand data requirements and deliver scalable solutions.
• Perform performance tuning of databases, Spark jobs, and data pipelines.
• Support production deployments and provide ongoing maintenance for critical data systems.
• Follow best practices for coding, documentation, testing, and release management.

Nice to Have:

• Experience with cloud platforms such as Azure, AWS, or GCP.
• Familiarity with CI/CD pipelines and DevOps practices for data platforms.
• Knowledge of data governance, metadata management, and security best practices.
• Experience supporting large-scale enterprise data environments.

Skills:

• SQL.
• Python.
• SSIS.
• Airflow.
• Spark.
• Iceberg.
• Databricks.
• ETL/ELT.
• Data Modeling.
• Data Warehousing.

Qualification And Education:

• Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field.