Academic Professionals Transition Program to Data Engineer

8 hours ago


Pune, Maharashtra, India Atgeir Solutions Full time

Experience Required: 5–10 years

Location:PUNE

Industry:
 IT Consulting & Services

Education:
 Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

Background
: Teaching in Engineering College in relevant subjects

Role Overview

We are launching a unique opportunity designed specifically for 
professors, lecturers, and educators
 who are passionate about transitioning into the IT industry, particularly in the field of 
Data Engineering
.

This is the 
structured transition pathway
 where you'll receive hands-on training, mentoring, and real-world project experience to build a solid foundation in data engineering.

No prior IT industry experience is required — 
only a strong analytical mindset, willingness to learn, and teaching/academic experience
.

Who Should Apply

This role is 
exclusively open to
:

  • Professors, lecturers, researchers, or teaching professionals 
    currently or recently
     employed at an educational institute.
  • Candidates with a strong interest in switching to the IT/Data field.
  • Individuals who are 
    self-motivated
    , quick learners, and eager to apply their academic skills in a tech environment.

As a Data Engineer at Atgeir Solutions, you will play a pivotal role in designing, developing, and optimizing our data infrastructure and workflows. Your academic expertise will contribute to building robust systems that drive insightful analytics, enhance AI/ML models, and enable data-driven decision-making for our clients.

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.

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