Data/QA Engineer _ AWS Cloud & Agentic AI- Remote -Contract

Duration: 3 months (May extend to longer term)

Work Hours: 12:30 to 9:30 IST

Requirement: Should be very good in written and oral communication and should be strong in technology and tools

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.).

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)