Data Engineer
Responsibilities:
• Design and maintain end-to-end data pipelines that process large and complex datasets with a focus on performance and reliability.
• Develop ETL workflows using Python and Spark to transform raw data into structured, usable formats for downstream consumption.
• Work with Hadoop-based environments to manage distributed data processing and storage activities at scale.
• Integrate streaming and messaging components such as Kafka to support near real-time data ingestion and event-driven processing.
• Monitor pipeline health, troubleshoot data issues, and implement improvements that strengthen stability and data quality.
• Collaborate with analysts, developers, and other stakeholders to understand data needs and translate them into technical solutions.
• Improve existing data architecture by refining workflows, reducing processing bottlenecks, and increasing operational efficiency.
Qualifications:
• Hands-on experience building data engineering solutions with Apache Spark in production environments.• Strong programming ability in Python for data processing, automation, and pipeline development.
• Working knowledge of Apache Hadoop and distributed data ecosystems.
• Experience using Apache Kafka for data streaming or message-based data integration.
• Solid understanding of ETL design principles, including extraction, transformation, validation, and loading processes.
• Ability to work with large datasets while maintaining accuracy, scalability, and performance.
• Strong problem-solving skills and the ability to communicate technical concepts clearly across teams.
Compensation
$67.45-$78.10 HourlyAbout Us
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