Data Engineer
3 weeks ago
We are seeking a highly skilled Data Engineer to join our team at Cognizant. As a Data Engineer, you will be responsible for designing, developing, and deploying large-scale data pipelines and architectures.
Key Responsibilities:- Data Pipeline Development: Design, build, and maintain efficient and reliable large-scale batch and real-time data pipelines with data processing frameworks.
- Data Integration: Integrate and collate data silos in a scalable and compliant manner.
- Collaboration: Collaborate with cross-functional teams, including Project Managers, Data Architects, Business Analysts, and Frontend Developers, to build scalable data-driven products.
- API Development: Develop backend APIs and work on databases to support applications.
- Agile Environment: Work in an Agile Environment that practices Continuous Integration and Delivery.
- Proficiency in Data Cleaning and Transformation: Proficient in general data cleaning and transformation (e.g., SQL, pandas, R, etc.).
- ETL Pipeline Development: Proficient in building ETL pipeline (e.g., SQL Server Integration Services (SSIS), AWS Database Migration Services (DMS), Python, AWS Lambda, ECS Container task, Eventbridge, AWS Glue, Spring).
- Database Design and Experience: Proficient in database design and various databases (e.g., SQL, PostgreSQL, AWS S3, Athena, MongoDB, Postgres/gis, MYSQL, SQLite, voltdb, Cassandra, etc.).
- Cloud Experience: Experience in cloud technologies such as GPC, GCC (i.e., AWS, Azure, Google Cloud).
- Big Data Environment: Experience and passion for data engineering in a big data environment using Cloud platforms such as GPC, GCC (i.e., AWS, Azure, Google Cloud).
- Production-Grade Data Pipelines: Experience with building production-grade data pipelines, ETL/ELT data integration.
- System Design and Data Structure: Knowledge about system design, data structure, and algorithms.
- Data Modelling and Storage Infrastructure: Familiar with data modelling, data access, and data storage infrastructure like Data Mart, Data Lake, Data Virtualization, and Data Warehouse.
- API and Web Requests: Familiar with rest API and web requests/protocols in general.
- Big Data Frameworks and Tools: Familiar with big data frameworks and tools (e.g., Hadoop, Spark, Kafka, RabbitMQ).
- Web Scraping: Familiar with W3C Document Object Model and customized web scraping (e.g., BeautifulSoup, CasperJS, PhantomJS, Selenium, Nodejs, etc.).
- Scripting Language: Comfortable in at least one scripting language (e.g., SQL, Python).
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