Cloud Data Engineer

3 hours ago

Kurnool, Andhra Pradesh, India beBeeData Full time ₹ 25,00,000 - ₹ 40,00,000
Job Title: Cloud Data Architect

Agentic AI

We are seeking skilled professionals to design and build robust data pipelines that power Agentic AI solutions on AWS. The role focuses on developing efficient ETL/ELT workflows, ensuring data quality, security, and scalability to support AI/ML model training, inference, and intelligent decision-making processes in a cloud-native environment.

Data Ingestion and Processing: Design, develop, and maintain ETL/ELT pipelines to process structured, unstructured, and streaming data in AWS environments.

  • Develop scalable data ingestion architectures utilizing AWS services such as S3, Glue, EMR, Lambda, Kinesis, Step Functions, Redshift, Athena, DynamoDB, RDS, and Lake Formation.
  • Implement robust data governance, security, and quality controls throughout the data lifecycle, supporting regulatory compliance.
  • Enable both real-time and batch data processing pipelines to power AI-driven workflows and applications.
  • Collaborate closely with AI/ML teams to prepare clean, high-quality datasets optimized for model training, inference, and fine-tuning.
  • Optimize data infrastructure to achieve high performance, scalability, reliability, and cost efficiency.
  • Implement CI/CD pipelines and data engineering best practices in a cloud-native architecture for automated deployments.
  • Monitor, debug, and resolve issues in data pipelines to ensure high availability, reliability, and minimal latency.
  • Maintain thorough documentation of data pipelines, architecture, and processes to facilitate reproducibility and knowledge transfer.

Required Skills & Qualifications:

  • Hands-on experience in Data Engineering, building data pipelines, and cloud-based data solutions.
  • Strong programming skills in Python, SQL, and/or Scala/Java.
  • Expertise in AWS cloud services, including S3, Glue, EMR, Redshift, Athena, Kinesis, Lambda, Step Functions, DynamoDB, and RDS.
  • Experience with data pipeline orchestration tools such as Apache Airflow, AWS Step Functions, Dagster, or similar.
  • Proficiency in big data frameworks like Apache Spark, Hadoop, or Flink.
  • Solid understanding of data modeling, data warehousing concepts, and schema design principles.
  • In-depth knowledge of data governance, lineage, security policies (IAM roles, encryption, Lake Formation).
  • Hands-on experience in managing real-time streaming data using Kafka, Kinesis, or equivalent tools.
  • Familiarity with DevOps practices, including Infrastructure as Code (Terraform, CloudFormation), CI/CD automation, Git, and containerization (Docker).

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