Job Description
Job Summary: As a Platform Engineer, you will play a key role in developing, deploying, and maintaining robust cloud infrastructure and platforms that support data processing, analytics, and machine learning operations. Your expertise in AWS, Azure, and advanced data processing tools like Power BI, Databricks, and SQL will enable you to create scalable, efficient systems to meet our data and ML needs. You will collaborate with cross-functional teams to develop solutions that drive data insights and enhance decision-making.
Duration: 6 months
Day Rate: $+40 per day
Location: Melbourne
Flebile working: 1-2 days in the office, per week
Key Responsibilities
Design, implement, and maintain cloud-based platforms and infrastructures, primarily in AWS and Azure environments.
Develop, deploy, and manage machine learning models using AWS SageMaker and Azure ML to improve business intelligence and decision-making processes.
Manage and integrate large datasets from various sources using SQL, Databricks, and other data management tools.
Implement and optimize data pipelines to support ML workflows, from data ingestion to model deployment.
Collaborate with data scientists and engineers to build and optimize ML models, ensuring they are scalable, efficient, and deployable.
Build and manage Power BI reports and dashboards, integrating them with cloud data sources for real-time insights and decision-making.
Work closely with cross-functional teams to identify opportunities to enhance data platforms and improve analytics capabilities.
Leverage Text Analytics tools to process and derive insights from unstructured data sources such as text documents, social media feeds, and customer interactions.
Ensure security, compliance, and best practices in platform design and implementation.
Provide operational support for cloud infrastructure and services, ensuring high availability and performance.
Required Skills & Qualifications
Strong experience with AWS cloud infrastructure and services (e.g., EC2, S3, RDS, Lambda, SageMaker).
Experience working with Azure cloud services, particularly in ML model development and deployment (Azure ML, Cognitive Services).
Proficient in SQL for querying and manipulating large datasets.
Hands-on experience with Databricks and building data pipelines for big data processing.
Solid understanding of machine learning concepts and experience in developing and deploying ML models.
Familiarity with Text Analytics and Natural Language Processing (NLP) for deriving insights from unstructured data.
Expertise in Power BI for creating, managing, and optimizing business intelligence reports and dashboards.
Knowledge of data architecture principles, data governance, and cloud best practices.
Strong programming skills in Python, R, or other relevant languages for building data pipelines and ML workflows.
Experience with version control systems like Git and CI/CD processes for managing code and deployment.
Ability to work in a fast-paced environment with minimal supervision and meet deadlines.
Please click apply today to be apart of the amazing large scale project!
Duration: 6 months
Day Rate: $+40 per day
Location: Melbourne
Flebile working: 1-2 days in the office, per week
Key Responsibilities
Design, implement, and maintain cloud-based platforms and infrastructures, primarily in AWS and Azure environments.
Develop, deploy, and manage machine learning models using AWS SageMaker and Azure ML to improve business intelligence and decision-making processes.
Manage and integrate large datasets from various sources using SQL, Databricks, and other data management tools.
Implement and optimize data pipelines to support ML workflows, from data ingestion to model deployment.
Collaborate with data scientists and engineers to build and optimize ML models, ensuring they are scalable, efficient, and deployable.
Build and manage Power BI reports and dashboards, integrating them with cloud data sources for real-time insights and decision-making.
Work closely with cross-functional teams to identify opportunities to enhance data platforms and improve analytics capabilities.
Leverage Text Analytics tools to process and derive insights from unstructured data sources such as text documents, social media feeds, and customer interactions.
Ensure security, compliance, and best practices in platform design and implementation.
Provide operational support for cloud infrastructure and services, ensuring high availability and performance.
Required Skills & Qualifications
Strong experience with AWS cloud infrastructure and services (e.g., EC2, S3, RDS, Lambda, SageMaker).
Experience working with Azure cloud services, particularly in ML model development and deployment (Azure ML, Cognitive Services).
Proficient in SQL for querying and manipulating large datasets.
Hands-on experience with Databricks and building data pipelines for big data processing.
Solid understanding of machine learning concepts and experience in developing and deploying ML models.
Familiarity with Text Analytics and Natural Language Processing (NLP) for deriving insights from unstructured data.
Expertise in Power BI for creating, managing, and optimizing business intelligence reports and dashboards.
Knowledge of data architecture principles, data governance, and cloud best practices.
Strong programming skills in Python, R, or other relevant languages for building data pipelines and ML workflows.
Experience with version control systems like Git and CI/CD processes for managing code and deployment.
Ability to work in a fast-paced environment with minimal supervision and meet deadlines.
Please click apply today to be apart of the amazing large scale project!
💡 Quick Summary
Seeking a career-building opportunity? The Platform Engineer position is now open for candidates interested in the Software Developer Jobs sector. This role in Melbourne offers a professional environment and growth potential.
Requirement Snapshot: Candidates should possess basic communication skills, a proactive attitude, and the ability to work in a team. Experience in Software Developer Jobs is a plus.
