Data/QA Engineer _ AWS Cloud
1 day ago
Job Description: QA Engineer (Agentic AI AWS)
Key Responsibilities
- Develop and execute test plans, test cases, and automation scripts for AI-powered applications on AWS.
- Test data pipelines (ETL/ELT, streaming, batch) for correctness, completeness, and performance.
- Validate AI/ML workflows (model training, inference, fine-tuning, and retraining pipelines).
- Perform functional, regression, integration, performance, and security testing of cloud-based applications.
- Create automated test frameworks for APIs, microservices, and AWS Lambda functions.
- Implement continuous testing practices within CI/CD pipelines.
- Collaborate with data engineers, ML engineers, and solution architects to ensure end-to-end system quality.
- Monitor production systems and validate results against expected business/AI outcomes.
- Document test results, defects, and provide feedback for process improvements.
Required Skills & Qualifications
- 3-7 years of QA/testing experience (depending on seniority level).
- Strong knowledge of manual and automated testing practices.
- Strong skills in Python/Java/JavaScript for automation.
- Experience testing cloud-native applications on AWS (S3, Lambda, Step Functions, API Gateway, ECS, DynamoDB, etc.).
- Familiarity with CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI/CD, or CodePipeline).
- Understanding of data validation and data quality frameworks (Great Expectations, Deequ, etc.).
Job Description: Data Engineer (Agentic AI AWS)
Key Responsibilities
- Design, build, and maintain ETL/ELT pipelines for structured and unstructured data in AWS.
- Work with AWS services (e.g., S3, Glue, EMR, Lambda, Kinesis, Step Functions, Redshift, Athena, DynamoDB, RDS) to manage data ingestion, transformation, and storage.
- Ensure data quality, governance, and security across the full lifecycle.
- Enable real-time and batch data processing to support AI-driven workflows.
- Collaborate with AI/ML teams to prepare datasets for model training, inference, and fine-tuning.
- Optimize data infrastructure for scalability, cost-efficiency, and performance.
- Implement CI/CD pipelines and best practices for data engineering in a cloud-native environment.
- Monitor and troubleshoot data pipelines to ensure high availability and reliability.
Required Skills & Qualifications
- 37 years of experience in data engineering (adjust based on seniority).
- Strong programming skills in Python, SQL, and/or Scala/Java.
- Expertise in AWS cloud ecosystem (S3, Glue, EMR, Redshift, Athena, Kinesis, Lambda, etc.).
- Experience with data pipeline orchestration tools (Airflow, Step Functions, Dagster, or similar).
- Proficiency with big data frameworks (Spark, Hadoop, or Flink).
- Familiarity with data modeling, warehousing, and schema design.
- Solid understanding of data governance, lineage, and security (IAM, Lake Formation, encryption).
- Experience with real-time streaming data (Kafka, Kinesis, or equivalent).
- Knowledge of DevOps practices (Terraform/CloudFormation, CI/CD, Git, Docker)
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