Key skills
AWS Data ServicesAdvanced PythonData EngineeringContainerized DeploymentsAPI IntegrationWorkflow OrchestrationDevOps MindsetData Security PracticesProblem-solvingTechnical CommunicationPythonAILLMPandasRedshiftSparkAirflowApache AirflowdbtAWSDockerGlueBedrockLeadershipPrototypingCommunication
About this role
FlairX is seeking a highly hands-on AWS Data & AI Platform Engineer to support their Enterprise Architecture organization. The role focuses on developing cloud-based data and AI reference implementations, with a significant emphasis on engineering execution and advanced Python skills.
Responsibilities:
- Design and implement cloud-native data solutions using AWS services
- Develop scalable data pipelines and processing workflows using Python
- Build reference implementations demonstrating modern AWS data platform capabilities
- Support enterprise modernization initiatives through solution prototyping aligned with architecture strategy
- Develop programmatic AI agents that integrate with enterprise APIs and data platforms
- Implement AI-enabled automation solutions leveraging enterprise datasets
- Support integration of AI services into enterprise data and application ecosystems
- Deliver proof-of-concept and reference implementations as directed by Enterprise Architecture leadership
- Translate architectural concepts into working technical solutions
- Demonstrate scalable and reusable architecture patterns for enterprise adoption
- Develop and deploy containerized workloads using Docker or similar technologies
- Support cloud-native deployment and automation practices
- Contribute to building scalable, reliable, and maintainable platforms
Requirements:
- 8–10+ years of experience in data engineering, cloud engineering, or platform development
- Strong hands-on experience with AWS Data Services, including: AWS Glue, Amazon Redshift, AWS Bedrock
- Advanced Python development experience in data engineering or automation environments
- Experience building AWS-based data pipelines and processing solutions
- Experience integrating systems via APIs and data services
- Experience with containerized deployments
- Strong problem-solving and technical communication skills
- Ability to convert architectural direction into working implementations
- Experience with workflow orchestration tools such as: Apache Airflow, dbt
- Experience with data processing frameworks and libraries: Spark, Pandas, Polars
- Experience working with modern data formats and open data architectures
- Exposure to AI or LLM-based automation solutions
- DevOps-oriented development mindset
- Experience implementing data security practices, including: Data masking, Encryption, Role-based access control