13 Collabera Technologies Jobs
Data Engineering Manager - Spark/Hadoop (10-12 yrs)
Collabera Technologies
posted 20d ago
Flexible timing
Key skills for the job
Role Description :
This is a full-time on-site role for a Data Engineering Manager at Collabera Digital in Pune. The Data Engineering Manager will be responsible for engineering management, team leadership, software development, project management, and integration on a day-to-day basis.
Position Details:.
Job Title : Data Engineering Manager.
Experience:- 10+ Years.
Location : 4 months WFH after that WFO from Chennai Or Mumbai.
Joining : Immediate to 15 days.
Job Description :
BASIC QUALIFICATIONS :
These are the fundamental requirements for the position, and candidates must meet most or all of them.
Education :
- Bachelor's degree in relevant fields: A degree in computer science, information technology, software engineering, or closely related fields (e., Data Science, Computer Engineering, or Information Systems).
- Experience in Data Engineering/Programming
5+ years of hands-on experience: Practical experience is necessary in areas like :
- SQL : Proficiency in SQL programming, including writing and debugging stored procedures, functions, and views.
- Python & Object-Oriented Programming : Familiarity with Python for scripting and building data pipelines.
- Object-oriented programming experience with languages like Java or C++ is also important.
- Knowledge of Data Engineering Tools/Frameworks
- Experience with modern technologies in data engineering
- Snowflake : A cloud-based data warehousing platform.
- Redshift : Amazon's cloud data warehouse solution.
- Spark : A distributed data processing engine.
- Airflow : An open-source tool for orchestrating complex workflows.
- Hadoop & Kafka: Big data tools for storage and real-time data streaming.
Cloud-Based Analytics Ecosystem :
- Cloud experience, particularly with platforms like AWS (Amazon Web Services) and Snowflake.
- This suggests the ability to work in cloud environments to process and store data.
Understanding of Development Lifecycles :
- Knowledge of SDLC (Software Development Life Cycle) and data science development lifecycle (CRISP).
- CRISP refers to the Cross-Industry Standard Process for Data Mining, which is a widely used methodology for data science projects.
PREFERRED QUALIFICATIONS :
- These are additional skills or qualifications that are not mandatory but would make a candidate more competitive.
Advanced Education :
- An advanced degree (Master's or PhD) in Data Science, Computer Engineering, or similar fields is preferred, but not required.
Data Science Technologies :
- Experience with platforms that facilitate data science, like Dataiku, AWS SageMaker, or similar tools.
- These platforms are used for building, deploying, and managing machine learning models.
Machine Learning & AI :
- Familiarity with machine learning and AI technologies, especially in how they integrate with data engineering pipelines.
- This shows an understanding of how to integrate data engineering with machine learning models or artificial intelligence applications.
Containerization and Orchestration :
- Experience with Docker (a tool for creating containers for applications) and Kubernetes (a platform for managing containerized applications).
- These tools are used for managing scalable applications in modern cloud environments.
Remote Team Experience :
- Experience working in a distributed remote team environment, which is increasingly common in global or tech-centric organizations.
Agile Practices :
- Hands-on experience with Agile methodologies (e. Scrum), which is a framework for iterative development in software engineering and data science projects.
Functional Areas: Other
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