Data Engineer

Apexvortex Solutions seeks a meticulous and skilled Data Engineer to join our team and enhance our data infrastructure. This role involves designing, building, and maintaining scalable data pipelines and warehousing solutions that support the organizations data needs. The Data Engineer will ensure the integrity, security, and accessibility of data by implementing best practices in data modeling, integration, and governance. The successful candidate will work closely with multiple teams to gather, process, and optimize data from various sources, enabling data-driven decision-making across the company. The ideal candidate has 2 to 5 years of hands-on experience in data engineering, demonstrating strong proficiency in SQL, Python, and ETL processes. Experience with Apache Airflow and Spark, as well as familiarity with cloud data services from providers such as AWS, Azure, or Google Cloud Platform, is essential. While this role does not include team management responsibilities, the Data Engineer will be expected to collaborate effectively and proactively troubleshoot issues to maintain the reliability and performance of data pipelines.
Responsibilities
 

• Design, build, and maintain scalable and efficient data pipelines using ETL or ELT methodologies to process structured and unstructured data.
• Develop and manage data warehousing solutions capable of supporting large-scale data storage and querying requirements.
• Build and orchestrate complex workflows using Apache Airflow to automate and monitor data processing jobs.
• Integrate data from multiple heterogeneous sources including APIs, various databases, and cloud platforms to centralize data access.
• Optimize SQL queries and tune pipeline performance to ensure timely and reliable data availability.
• Apply data modeling best practices to improve data storage efficiency and facilitate fast and accurate data retrieval.
• Ensure consistent data quality, governance, and security standards across all data pipelines and systems.
• Continuously monitor pipeline reliability and perform proactive troubleshooting to identify and resolve data issues before they impact business operations.
• Collaborate with analytics, engineering, and product teams to align data infrastructure with business requirements.
• Document data pipeline architecture, procedures, and industry best practices to maintain knowledge sharing and operational continuity.