Core Competencies
* Proficiency with AWS SageMaker Unified Studio, including its various components Discover, Build and Govern.
* Experience with development in Sagemaker IDE and Application (Jupyterlab, Spaces and Partner AI Apps).
* Data Analysis and Integrations ( Query Editor, Visual ETL Jobs and Data Processing jobs)
* Orchestration of workflows and ML Pipelines
* Understanding and expertise to work with ML and Gen AI tools available in unified studio are ad-on values.
* Setting up projects and data governance in SageMaker Unified studio
* Framework Development: Proven ability to design, develop, and implement highly reusable and adaptable data ingestion frameworks capable of handling diverse source types (e.g., databases, APIs, message queues, file systems).
* Low-Latency Real-time: Deep expertise in building real-time data pipelines with stringent sub-second latency requirements, utilising services like AWS Kinesis (Data Streams/Firehose), Apache Kafka, or similar streaming technologies.
* Batch Processing: Experience with robust batch ingestion patterns and tools (e.g., AWS Glue, Apache Spark) for efficient processing of larger datasets.
* Data Transformation: Strong skills in designing and implementing efficient data transformation logic for both streaming and batch data.
Programming and Scripting
* Advanced proficiency in Python, particularly for developing scalable data ingestion and export frameworks, API integration, and extensive use of the AWS SDK (Boto3).
* Experience with performance optimisation techniques for Python applications in data-intensive environments.
* Familiarity with other relevant languages (e.g., Scala, Spark) for high-performance streaming applications is beneficial.
AWS Services for Data and Infrastructure
* In-depth knowledge of core AWS services: Lambda, S3, DynamoDB, CloudWatch, SQS, SNS, and API Gateway.
* Strong understanding of AWS networking (VPC, security groups, private endpoints) and IAM for secure, fine-grained access control.
Databases (Relational and No-SQL)
* Expertise with Amazon RDS (Relational Database Service) for both real-time data ingestion and efficient batch export. This includes optimising database performance, connection pooling, and transaction management for high-throughput, low-latency operations.
* Proficiency with Amazon Redshift for large-scale data warehousing, including data loading strategies (e.g., COPY command), query optimisation, and managing Redshift clusters for analytical workloads and batch export.
* Experience with No-SQL databases such as Amazon DynamoDB, MongoDB
Orchestration and Workflow Management
* Experience with AWS Managed Apache Airflow (MWAA) for orchestrating complex data pipelines, scheduling batch jobs, and managing dependencies between ingestion, processing, and export tasks.
* Ability to write, deploy, and manage Airflow DAGs (Directed Acyclic Graphs) for robust workflow automation.
Monitoring and Observability (Real-time Heartbeat Export)
* Ability to design and implement comprehensive real-time monitoring solutions, including custom metrics, detailed logging, and tracing.
* Experience with AWS CloudWatch for collecting, analysing, and acting on operational data, specifically for generating and exporting \"heartbeat\" signals to external systems or dashboards.
* Knowledge of setting up proactive alerts and automated notifications for system health, performance degradation, and data pipeline anomalies.
Software Development Practices & Architecture
* Strong understanding of software engineering principles, design patterns, and architectural best practices for building scalable, maintainable, and reusable data frameworks.
* Proficiency with version control systems (Git) and collaborative development workflows.
* Experience with CI/CD pipelines for automated testing, deployment, and release management of data ingestion and export solutions.
* Familiarity with Infrastructure as Code (e.g., AWS CloudFormation, Terraform) for managing and provisioning AWS resources
Key Skillset
* 1. Previous experience in developing framework for batch and real time data ingestions in relation and no-sql databases or filesystems.
* 2. Previous experience in Real time data ingestion with low latency with experience on AWS Kinesis Stream, Apache Kafka or similar streaming technologies
* 4. Sound knowledge and experience in building Data Warehouses and Data Lakehouses.
* 5. Data Modeling experience is a plus point
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