Work Set-up: Remote

Schedule: 10am-6pm SGT

Responsibilities:

• 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 on AWS. This includes data ingestion, transformation, storage, and consumption layers.
• Confluent Kafka Expertise: Design, implement, and optimize highly performant and reliable data streaming solutions using Confluent Platform (Kafka, ksqlDB, Kafka Connect, Schema Registry). Ensure efficient data flow for real-time analytics and AI applications.
• AWS Cloud Native Development: Develop and deploy data solutions leveraging a wide range 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 related quantitative field.
• 3 to 5 years of experience in data engineering, with a significant focus on cloud-based solutions.
• Strong expertise in AWS data services (S3, Glue, EMR, Redshift, Kinesis, Lambda, etc.).
• Extensive hands-on experience with Confluent Platform/Apache Kafka for building real-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 (star schema, snowflake schema, etc.).
• Experience with CI/CD pipelines and DevOps practices (Git, Terraform, Jenkins, Azure DevOps, 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