Data & AI Engineer

Overview

As a Data & AI Engineer at Accenture, you will contribute to cloud-based data and platform solutions across leading platforms and instruments. You’ll work within AI-augmented delivery, using GenAI tooling to accelerate engineering, testing and documentation. You’ll collaborate with architects, product engineers and stakeholders to deliver scalable data products and AI-enabled infrastructure. This role offers hands-on growth in a global, innovation-driven environment with opportunities to influence how data supports AI systems.

Pay / Benefits

• 25 days’ vacation per year
• private medical insurance
• 3 extra days’ leave per year for charitable work

Responsibilities

• Build and run cloud data and platform solutions on Azure, AWS, GCP, Databricks, Microsoft Fabric, Snowflake
• Develop and maintain scalable batch and streaming data pipelines (ETL/ELT) at scale
• Create and manage cloud infrastructure with IaC tools (Terraform, Bicep, CloudFormation) for automated provisioning
• Implement CI/CD pipelines for data/platform delivery using Git, Azure DevOps or GitHub Actions with AI-assisted testing/documentation
• Apply data modeling (Kimball, medallion) using AI tooling for iterative models and validation
• Implement data quality, lineage, observability, and self-describing pipelines
• Support cloud platform operations including networking, IAM, security and policy adherence
• Leverage GenAI/AI-assisted development tools to accelerate delivery and validate outputs
• Develop understanding of agentic AI concepts to inform architecture and design for agentic consumption
• Collaborate with architects, engineers and stakeholders to meet technical and business needs

Key requirements

• Cloud engineering experience across one or more of: Azure, AWS, GCP, Databricks, Microsoft Fabric or Snowflake
• Python and SQL with strong engineering fundamentals
• Experience with batch and/or streaming pipelines or cloud infrastructure and platform engineering
• CI/CD tooling for data/platform delivery (Git, Azure DevOps, GitHub Actions)
• Practical use of GenAI or AI-assisted development tools in daily work
• Familiarity with AI-ready architecture concepts affecting quality, latency, security and lineage
• Basic understanding of agentic AI concepts and how autonomous systems interact with data and platform infrastructure

• curiosity
• driven
• team collaboration
• Azure
• AWS
• GCP

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