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Remote, common mid-level Data Engineer role attracts many qualified applicants.
Core SQL, Python, ETL, and data-platform skills are broadly transferable across industries.
Mandatory 2–3 years plus SQL, Python, PySpark, ETL, and cloud skills enforce strict filtering.
Design, develop, and maintain scalable ETL/ELT data pipelines integrating data from multiple structured and unstructured sources.
Ensure data accuracy, quality, performance, and security while supporting analytics, AI, and machine learning applications.
Monitor, troubleshoot, and optimize data workflows using tools like Apache Airflow, PySpark, and cloud platforms to enable reliable data solutions.
2–3 years of professional experience in data engineering, ETL development, or related roles.
Proficiency in SQL, Python, PySpark, and experience with relational and NoSQL databases.
Experience with cloud data platforms (AWS, Azure, GCP) and data pipeline orchestration tools such as Apache Airflow.
Bachelor’s or Master’s degree in Computer Science, IT, Data Science, Software Engineering, or related field.
Experienced in building data pipelines supportive of AI and machine learning use cases including handling structured and unstructured data.
Comfortable working in cross-functional teams involving data scientists, AI engineers, and product teams with focus on data accuracy and performance.
Familiar with cloud-native data engineering tools, version-controlled development workflows, and advanced query optimization techniques.
