Senior Data Engineer (AWS & Confluent Data/AI Projects) | Remote

Work Set-up: RemoteSchedule: 10am-6pm SGTResponsibilities: Architect and Design Data Solutions: Lead the design and architecture of scalable,secure, and efficient data pipelines for both batch and real-time data processing onAWS. This includes data ingestion, transformation, storage, and consumption layers. Confluent Kafka Expertise: Design, implement, and optimize highly performant andreliable data streaming solutions using Confluent Platform (Kafka, ksqlDB, KafkaConnect, Schema Registry). Ensure efficient data flow for real-time analytics and AIapplications. AWS Cloud Native Development: Develop and deploy data solutions leveraging a widerange of AWS services, including but not limited to: Data Storage: S3 (Data Lake), RDS, DynamoDB, Redshift, Lake Formation. Data Processing: Glue, EMR (Spark), Lambda, Kinesis, MSK (for Kafka integration). Orchestration: AWS Step Functions, Airflow (on EC2 or MWAA) Analytics & ML: Athena, QuickSight, SageMaker (for MLOps integration). Required Skills and Qualifications: Bachelor's or Master's degree in Computer Science, Software Engineering, or a relatedquantitative field. 3 to 5 years of experience in data engineering, with a significant focus on cloud-basedsolutions. Strong expertise in AWS data services (S3, Glue, EMR, Redshift, Kinesis, Lambda, etc.). Extensive hands-on experience with Confluent Platform/Apache Kafka for buildingreal-time data streaming applications. Proficiency in programming languages such as Python, PySpark, Scala, or Java. Expertise in SQL and experience with various database systems (relational and NoSQL). Solid understanding of data warehousing, data lakes, and data modeling concepts (starschema, snowflake schema, etc.). Experience with CI/CD pipelines and DevOps practices (Git, Terraform, Jenkins, AzureDevOps, or similar). AWS Certifications (e.g., AWS Certified Data Analytics - Specialty, AWS Certified Preferred Qualifications (Nice to Have): Solutions Architect - Associate/Professional). Experience with other streaming technologies (e.g., Flink). Knowledge of containerization technologies (Docker, Kubernetes). Familiarity with Data Mesh or Data Fabric concepts. Experience with data visualization tools (e.g., Tableau, Power BI, QuickSight). Understanding of MLOps principles and tools. Candidate must have a working laptop Originally posted on Himalayas