Cloud & DevOps

 AWS, Azure, GCP (Certification is highly valued), Kubernetes (K8s), Docker, Terraform, Ansible, Jenkins, CI/CD pipelines, DevSecOps. 

 Data & AI/ML 

 Python, Generative AI (GenAI) concepts (Prompt Engineering), Machine Learning (ML), Data Engineering (e.g., Spark, Kafka), AIOps solutions. 

 Cloud-Native & Full-Stack 

 Microservices Architecture, APIs, Spring Boot, Java/J2EE, Python, Node.js, React, Angular, and strong foundational knowledge of Data Structures & Algorithms 

 Agile & Quality 

 Agile/Scrum/SAFe methodologies, Test Automation Frameworks (e.g., Selenium), Continuous Testing, Full-Stack Testing Engineers. 

 Legacy Mainframe 

 COBOL, JCL, DB2, IMS, experience with re-platforming/rehosting tools and methodologies (moving mainframe to cloud). 

 When Hiring comes to Data Engineering, the highest value will be in finding candidates who combine strong Data Engineering skills with Cloud Platform certification (AWS/Azure/GCP) and a foundational understanding of Python and AI/ML concepts 

This function is responsible for building and maintaining the massive data infrastructure.

• Cloud Data Services: Expertise in cloud-native data platforms is critical, primarily:
  
  • Azure: Azure Data Factory (ADF), Azure Synapse, Azure Databricks.
  • AWS: AWS Glue, Amazon Redshift, S3, EMR.
  • Google Cloud (GCP): BigQuery, Dataflow, Dataproc.
• Big Data Technologies: Proficiency in distributed processing frameworks:
  
  • Apache Spark, Apache Kafka, Hadoop.
• ETL Tools & Databases: Experience with traditional and modern tools:
  
  • Informatica, Talend, DataStage, Oracle, SQL Server, PostgreSQL.
• Programming Languages: Strong skills in Python (for scripting and data manipulation) and Scala (often used with Spark).

This function focuses on building predictive and prescriptive models to drive business value.

• Machine Learning (ML): Experience developing and deploying various ML models (supervised, unsupervised, deep learning).
• Generative AI (GenAI): This is a huge area of investment. We hire experts in Prompt Engineering, Large Language Models (LLMs), and building GenAI applications.
• Statistical Tools: Proficiency in Python (libraries like NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch) and R.
• Domain Expertise: Data Scientists who can apply models to specific industry problems (e.g., fraud detection in finance, demand forecasting in retail).

These skills are necessary for presenting complex data insights in an understandable format for clients.

• BI Tools: High proficiency in enterprise-level visualization tools:
  Power BI (Microsoft), Tableau, QlikView.
• Data Warehousing: Experience with modern data warehousing principles and tools like Snowflake and Teradata.

1. Digital Experience (CX/UX/UI). 

ReactJS, Angular, Vue.js, JavaScript, HTML5/CSS3, experience building conversational UI/UX. 

 2. Cloud & Accelerated Innovation 

 Cloud Platforms  - AWS, Azure, GCP—especially cloud-native services for serverless computing (e.g., AWS Lambda, Azure Functions). 

3.  DevOps & CI/CD 

 Kubernetes, Docker, Terraform, Azure DevOps, Jenkins, DevSecOps, creating and managing CI/CD pipelines. 

Hiring Focus on, who is responsible for migrating, managing, and modernizing a client's foundational IT—from legacy data centers to modern multi-cloud environments. 

 Hiring focus is heavily skewed toward certified Cloud Engineers and professionals who can automate infrastructure using code. 

 1. Core Cloud Platforms & Services

This is the most critical area, demanding professionals who are experts in the three major Hyperscalers:

• Amazon Web Services (AWS): Expertise in core services (EC2, S3, RDS, Lambda), networking, and security. Certification (Architect, DevOps, or SysOps) is highly valued.
• Microsoft Azure: Proficiency in Azure Compute, Networking, Azure Active Directory (Azure AD/IDAM), Azure Synapse, and Azure DevOps.
• Google Cloud Platform (GCP): Skills in Big Query, Compute Engine, and Cloud networking/security.
• Cloud Architecture: Architects capable of designing Hybrid Cloud and Multi-Cloud environments, ensuring seamless integration between client premises and public clouds.

 2. Infrastructure Automation & DevOps.

• Infrastructure as Code (IaC): Deep experience with provisioning tools like Terraform and Ansible.
  DevOps Tools: Proficiency in continuous integration/continuous delivery (CI/CD) pipelines using tools like Jenkins, GitLab, and Azure DevOps/Pipelines.
• Containerization: Expertise with orchestrating and managing container technologies like Kubernetes (K8s) and Docker.
• Scripting: Strong skills in Shell Scripting, Python, or PowerShell for automation tasks.

 3. Cloud Security & Identity Access Management (IAM) 

Security is often managed within the infrastructure unit, especially regarding cloud governance and identity.

• Cloud Security: Experience with cloud-native security services (e.g., AWS Security Hub, Azure Security Center) and security best practices for public cloud deployment.
• Cybersecurity/Network: Expertise in Firewalls (Palo Alto, Fortinet), DDoS protection, and Cloud Security solutions.
  
• IAM Tools: Proficiency with Identity and Access Management platforms like SailPoint, ForgeRock, or native cloud IAM services (Azure AD, AWS IAM).