Sr. AWS Data Engineer

Design, build, and operate scalable, cloud-native data platforms supporting batch and streaming use cases, with strong focus on governance, performance, and reliability., * Data quality and validation: Implementing data quality checks, reconciliation logic, and exception handling within pipelines

• Metadata-driven frameworks: Building configurable pipelines driven by metadata stored in Aurora or DynamoDB
• Logging and observability: Integrating CloudWatch logging, custom metrics, and alerting into data pipelines
• Unit and integration testing: Writing test cases for ETL logic using frameworks such as pytest
• Version control: Proficiency with Git for source code management, branching strategies, and code reviews

2. Python: Strong hands-on experience with Python for data engineering tasks, including scripting, automation, and ETL logic development
3. PySpark: Proficiency in writing and optimizing PySpark jobs for large-scale data transformations
4. SQL: Advanced SQL skills for data querying, transformation logic, and stored procedure conversion from SQL Server
5. Big Data Processing Frameworks
6. Apache Spark: Strong experience with Spark core concepts - RDDs, DataFrames, Datasets, partitioning, and performance tuning
7. Data partitioning and optimization: Experience with data skew handling, broadcast joins, caching strategies, and Spark tuning
8. AWS Services (Hands-On Experience Required)
9. AWS Glue ETL: Developing and deploying Glue jobs (Python Shell and Spark), job bookmarks, dynamic frames, and custom connectors
10. AWS Glue Data Catalog: Managing databases, tables, crawlers, classifiers, and schema versioning
11. AWS Lake Formation: Configuring data lake permissions, fine-grained access control, and data filtering
12. AWS Step Functions: Designing and implementing state machines for ETL workflow orchestration, error handling, and retry logic
13. AWS Lambda: Writing serverless functions for event-driven triggers, lightweight transformations, and pipeline utilities
14. Amazon Aurora: Working with Aurora PostgreSQL compatible for relational data storage and query optimization
15. Amazon DynamoDB: Designing and querying NoSQL tables
16. Amazon S3: Proficiency in S3 data lake design - partitioning strategies, storage classes, lifecycle policies, and S3 event notifications
17. AWS IAM: Understanding of roles, policies, and least-privilege access patterns relevant to data pipeline security
18. ETL Development & Migration
19. Informatica PowerCenter (working knowledge): Ability to read and interpret Informatica workflows, sessions, mappings, and transformations to support conversion to AWS Glue
20. ETL framework development: Experience building reusable, configurable ETL frameworks with logging, error handling, retry mechanisms, and metadata-driven execution
21. Data pipeline design patterns: Familiarity with incremental loads, CDC (Change Data Capture), full loads, and SCD (Slowly Changing Dimensions)
22. SQL Server (working knowledge): Ability to understand SQL Server schemas, stored procedures, and SSIS packages for migration analysis, * 8+ years in IT related role

• Strong hands on experience in AWS Cloud, SQL and Python
• Good experience with Kafka/Flink, AWS Glue and Airflow

At Cognizant, we strive to provide flexibility wherever possible, and we are here to support a healthy work-life balance though our various wellbeing programs. Based on this role's business requirements, this is an onsite position requiring 5 days a week in a client or Cognizant office in Charlotte, NC. Cognizant is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law.