AT

Atgeir

Academic Professionals Transition Program to Data Engineer

Pune · Senior

Older listing - lower visibility likelyVerified listingPosted 598d ago

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Skills required

21 listed

Data-Driven Decision-MakingGoogle Cloud Platform (GCP)Power BIMachine LearningResearch PapersExtract Transform Load (ETL)Application Programming Interface (API)Python (Programming Language)+13 more

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Job description

Location: Pune

Experience Required: 5–10 years

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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