Want to start your career in Big Data field? These Big data internships might help
Big Data is one of the most popular technologies in the world today. The demand for big data professionals is constantly increasing, which makes big data analytics a must-have skill in this data-driven world. Companies are looking for big data developers to enhance their business in the competitive world. So, what is the best way to kick-start your career in data analytics other than taking up a big data internship from reputed tech companies? This article features the top big data internships for you to apply in January 2022.
Big Data Engineer – Luxoft
Location – Bengaluru, Karnataka, India
Responsibilities
- Conceptualize and Implement the data pipeline based on the business requirement
- Able to fine-tune the existing Spark/Hive program
- Able to write test cases and enable CI/CD
- Able to identify and advise the client to improve the existing program
- Create and Maintain Data Modeling/Data Dictionary
- Able to write API to expose/consume the data if required
- Able to orchestrate the data pipeline and Integrate the ML/AI Models in the data pipeline
Business Analytics – Stirring Minds
Location – Work from home
Responsibilities
- Working on analyzing companies’ data and driving meaning out of the data after plotting the same across dashboards
- Work using various software tools that we use to measure our KPIs
Data Science – Stirring Minds
Location – Work from home
Responsibilities
- Work with Selenium and scrape a static and dynamic website
- Work on HTML and XPath
- Work on HTTP post and get requests
- Scrape using proxy servers (optional)
Data Analysis – Vista Rooms
Location – Mumbai, Maharashtra, India
Responsibilities
- Get actionable insights from data that can be used in real-time in all decision-making of the company
- Gather insights and implement various processes and changes across departments
- Gather insights & work hands-on with different teams to improve their various business metrics
- Create new models/improve existing models
Data Engineering – Blackcoffer
Location– Work from home
Responsibilities:
- Working on data engineering projects
- Working on data pipelines, data tools, automation, and databases
- Working on Azure ML and other ML tools
- Working on data warehouse, data platforms, data lake, and data integrations
- Working on Python programming and analytics using Python
- Working on Python API for the data analytics and ML
- Working on BI tools such as Google Data Studio, Kibana, PowerBI, and others
- Working on NoSQL databases such as MongoDB
- Working on databases such as Neo4j, PostgreSQL, Graph, and more
- Working with clients to lead projects
Big Data – IoT83
Location – Gurugram, Haryana, India
Responsibilities
- Writing high-performance, reliable, and maintainable code
- Developing and creating queries, views, and stored procedures for ETL processes and process automation
Data Analytics – Prakriti Textiles
Location – Work from home
Responsibilities
- Studying, implementing, and experimenting with new ideas
- Participating in periodic research review discussions
- Working with MS Excel & SQL to create data, and analyze the same for patterns and insights
- Performing initial analysis to assess the quality of the data and determine the price
- Performing further analysis to determine the meaning of the data
- Performing final analysis to provide additional data screening
- Preparing reports based on analysis and presenting them to management
Data Engineering Intern – Swific Technology Pvt. Ltd.
Location – Work from home
Responsibilities
- Contribute to the development and maintenance of the Business Intelligence platform to address the analytical needs of the organization.
- Understand business processes, identify the correct data sources and validate the quality of the data sources.
- Contribute to the development of data ingestion and transformation pipelines.
- Explore data and perform data analysis to support business requirements.
- Work with engineers to develop a high-quality data product.
- Interface with business representatives to proactively identify opportunities and gather requirements.
- Develop business acumen and cultivate stakeholder relationships.
- Analyze and visualize the business impact of data products.
- Document analytical methodologies used in the execution of data products.
- Constantly evolving as a Data Engineering and Analytics professional.
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