Data Engineer
hackajob Pune Division, Maharashtra, India
Job Description
"Unlock the future of banking as a Data Engineer at Barclays, where you'll drive innovation and operational excellence in digital infrastructure."
As a Data Engineer at Barclays, you'll be at the forefront of the bank's digital transformation, harnessing cutting-edge technology to build and manage robust, scalable, and secure digital infrastructure.
You'll collaborate with data scientists and other stakeholders to build and deploy machine learning models, supporting data-driven decision-making within the organization and driving business growth.
Why you should learn this:
High demand in the financial sector, with a projected growth rate of 14% in the next 5 years.
Expected Salary: £80,000 - £110,000 per annum, depending on experience.
How it works:
- Step 1: Build and manage robust, scalable, and secure digital infrastructure using cutting-edge technologies such as Java, Python, and automation tools.
- Step 2: Collaborate with data scientists and stakeholders to design, build, and deploy machine learning models, supporting data-driven decision-making.
Core Concepts to Master
Cloud Computing
Design and deploy cloud-based infrastructure using AWS, Azure, or Google Cloud Platform, ensuring scalability, security, and high availability.
Data Engineering Principles
Understand data engineering principles, including data ingestion, processing, storage, and analytics, to ensure data quality, integrity, and security.
Machine Learning
Design, build, and deploy machine learning models using Python, R, or other popular libraries, to support data-driven decision-making and business growth.
Automation and Scripting
Use automation tools such as Jenkins, GitLab CI/CD, or Ansible to automate deployment, testing, and monitoring of digital infrastructure, reducing manual effort and improving efficiency.
Java and Python Development
Develop robust, scalable, and secure software applications using Java and Python, following best practices and design patterns to ensure maintainability and reusability.
Interview Questions (Beginner)
- What is data engineering, and how does it relate to data science?
- Can you explain the difference between big data and small data?
- How do you ensure data quality and integrity in a data engineering pipeline?
Job Overview
Advance Questions
- • Design a data pipeline to ingest, process, and store 1TB of data per day using cloud-based infrastructure.
- • Explain how you would deploy a machine learning model to production using a containerization tool like Docker.
- • Describe a scenario where you would use automation tools to improve the efficiency of a data engineering pipeline.