Data Engineer

A Data Engineer in Cognizant’s AIA (AI & Analytics) practice brings relevant context to data for business and IT for intelligent systems and critical decisions. They work within the domains of data management, to integrate high-variation and/or high-velocity data to produce answers and enable clients to ask an entirely new class of questions. They will use a variety of tools and techniques to deliver better, faster, and intelligent data to some of the world’s most successful businesses – our clients.

Responsibilities

• Build and deliver data pipelines to store data in a way that is accessible, performant, secure, and sustainable
• Prototype solutions, prepare test scripts, and conduct tests for data replication, extraction, loading, cleansing, and data modeling for data warehouses
• Review and validate data loaded into data lakes/warehouses for accuracy
• Develop proofs of concept and evaluate design options to deliver ingestion, search, metadata cataloging and scheduling of data pipelines
• Data engineering in line with standard processes and Cognizant’s reference architecture
• Understand and detail technical use case requirements to deliver data movement and transformation solutions

Basic Qualifications

• Bachelor's degree in IT-related field and 0-3 years of IT experience
• Strong business interpersonal skills (including written and oral)
• Programming experience with Python, Python Pyspark or Java development for modern data engineering
• Strong background in relational data models
• Exposure in Data Pipelines & SQL / NoSQL (e.g. Cassandra, HBase) and Relational Database management systems
• Exposure to implementing PaaS services on public clouds - Azure, AWS, GCP, Databricks, Snowflake, SingleStore, Oracle, Informatica
• Strong problem solving and analytical thinking skills

Distinguishing Qualifications

• Full Data lifecycle development experience
• Industry experience (financial services, insurance, retail, healthcare, life sciences, communications)
• Experience in developing and deploying distributed computing applications using Open Source or cloud native services.
• Shown understanding of data applications including Hadoop components (HDFS, HBase, Hive, Sqoop, Flume, etc.)
• Exposure to data analytics and data mining tools/applications such as Tableau, QlikSense /QlikView, R or SAS
• Exposure to working with SQL, Relational Database Management Systems (e.g., SQL Server, Oracle)
• Development workflows (e.g., Microsoft VSTS)
• Experience leading teams