Data Engineer (academia)

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Experience Required:

Industry:

Education:

Background

Role Overview

Key Responsibilities

• Data Architecture and Pipeline Development:
• Design and implement scalable data architectures, ETL pipelines, and data workflows to process, store, and analyze large datasets effectively.
• Data Integration:
• Integrate and consolidate data from various sources, including APIs, databases, and cloud platforms, ensuring data integrity and quality.
• Data Governance and Security:
• Implement data governance practices, including data cataloging, lineage tracking, and ensuring compliance with information security standards (ISO 27001 or similar).
• Collaboration with Teams:
• Work closely with data scientists, software engineers, and business analysts to align data engineering solutions with business goals and requirements.
• Optimization:
• Optimize database performance, query execution, and storage solutions for efficiency and cost-effectiveness.
• Innovation and Mentorship:
• Contribute to innovative projects and mentor junior team members. Leverage your academic expertise to foster a culture of continuous learning and development.
  

Required Skills And Qualifications

• Technical Expertise:
• Proficiency in programming languages such as Python, Java, or Scala.
• Hands-on experience with big data technologies (e.g., Hadoop, Spark).
• Strong knowledge of SQL and NoSQL databases (e.g., PostgreSQL, MongoDB, Cassandra).
• Familiarity with data warehousing tools like Snowflake, Redshift, or BigQuery.
• Experience with cloud platforms like Google Cloud Platform (GCP), AWS, or Azure.
• Data Engineering Fundamentals:
• Expertise in ETL/ELT processes, data modeling, and data lake architectures.
• Knowledge of data visualization tools like Tableau, Power BI, or Looker.
• Educational/Academic Experience:
• Experience teaching relevant technical subjects (e.g., data engineering, databases, distributed systems, or machine learning) in engineering colleges.
• Proven ability to explain complex concepts to diverse audiences.
• Soft Skills:
• Excellent analytical and problem-solving skills.
• Strong interpersonal skills and the ability to collaborate effectively in cross-functional teams.
• Demonstrated ability to balance technical depth with business understanding.
  

Preferred Qualifications

• Certifications in cloud technologies (e.g., GCP Professional Data Engineer, AWS Certified Data Analytics).
• Familiarity with AI/ML tools and frameworks (e.g., TensorFlow, PyTorch).
• Contributions to research papers, technical blogs, or open-source projects.