Information Technology
Full-Time
AKIRA Insights
Overview
Key Responsibilities
S3 (Simple Storage Service) :
- Hands on experience in data related activities such as data parsing, cleansing quality definition data pipelines, storage and ETL scripts.
- Expert knowledge in AWS Data Lake implementation and support (S3, Glue, DMS Athena, Lambda, API Gateway, Redshift).
- Experiences in programming language Python/PySpark/SQL.
- Experience with data migration with hands-on experience.
- Experiences in consuming rest API using various authentication options with in AWS Lambda architecture.
- Orchestrate triggers, debug and schedule batch job using a AWS Glue, Lambda and step functions.
- Hands-on experience with Redshift, including data models, storage, and writing effective queries.
- Understanding of AWS security features such as IAM roles and policies.
- Knowledge of the Devops tools and CI/CD process.
- AWS certification in AWS is highly Skills & Expertise :
S3 (Simple Storage Service) :
- Data storage and partitioning, lifecycle policies, data replication, encryption at rest and transit, and data versioning, Data archive and Data sharing :
- Creation of Lambdas, configuring event-driven functions, monitoring, and integration with other AWS services of s3,Glue,API Gateway, Redshift etc.
- Creating & Managing ETL pipelines, Glue crawlers, job scheduling, and integration with S3, Redshift, and :
- Creating Tables, Views and Stored procedures, parameter tuning, workload management, configuring triggers, Redshift Spectrum usage for querying S3 data.
- Designing Restful APIs, securing endpoints using IAM or Cognito, throttling and logging API usage.
- Aware of existing VPC design, subnets, NAT gateways, peering, routing, and network ACLs for services creation.
- Configuring ALB/NLB for load distribution, health checks, sticky sessions, and SSL termination. CloudTrail :
- Enabling auditing and compliance logging, managing trails, integrating with CloudWatch and third-party SIEM :
- Knowledge about Sagemaker ML model training and deployment workflows, managing notebooks, endpoints,
- Managing version control, branching strategies, and automating workflows for testing and deployment.
- Building pipelines for build-test-deploy automation, plugin management, parallel execution, and integrations with SCM and artifact :
- Static code analysis, security vulnerability checks, technical debt reporting, integration into CI/CD.
- Understanding of ML model lifecycle : data preprocessing, training, evaluation, deployment, and monitoring.
- Experience supporting Data Scientists and ML Engineers in deploying models to production.
- Familiarity with tools and workflows like SageMaker, MLflow, Airflow (optional), and pipelines integration
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