Google Data Engineering

Role & responsibilities

• AWS SageMaker Unified Studio Expertise:
• 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
• Advanced Data Ingestion & Processing (Real-time & Batch):
• 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
• for high-performance, low-latency data storage and retrieval, particularly for real-time applications and feature serving.
• 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