Lead Data Engineer, AI & Modernization

About Accenture:

Accenture helps the world's leading enterprises reinvent by building their digital core and unleashing the power of AI to create value at speed for organizations across industries. Our strategy is to be the reinvention partner of choice for our clients and lead in the safe, widespread adoption of AI, and to be the most client-focused, AI-enabled, great place to work in the world.

We bring together the talent of our approximately 814,000 people with proprietary assets and platforms, deep process and industry expertise, and leading ecosystem relationships to deliver end-to-end solutions and measurable outcomes ough our Reinvention Services, we offer broad expertise across Cybersecurity, Digital Core, Finance, Industry and Enterprise, Song, Supply Chain and Engineering, and Talent, with advanced capabilities in AI and Data, Industry and Process, and Technology.

We serve approximately 9,000 clients and generated approximately $74 billion in FY26 revenue. Visit us at  Are:

Accenture is recognized as a global leader in AI and cloud transformation, helping businesses across industries reimagine the digital core for an AI-native operating model. Through partnerships with frontier AI labs such as Anthropic and OpenAI, and the leading cloud providers, Accenture delivers end-to-end services that drive innovation and business agility.

The Digital Core Reinvention practice builds the data foundations AI systems depend on — with a discovery edge. This seat anchors our archaeology techniques inside the data family: the foundation gets engineered from what is actually in the estate, not what the documentation claims.

You Are:

A data engineer who doesn't trust the documentation either — you'd rather mine the git history and trace the lineage than take a schema diagram at its word.

You belong here even if you haven't spent years in AI. Much of what makes a great practitioner in this work was learned somewhere adjacent — strong credentials in the neighboring discipline plus a year or two of serious hands-on work with LLM-based systems is exactly the profile we grow.

The Work:

You lead the data-engineering work on a migration engagement: pipelines and data products feeding AI systems (Data Pipelines and Data Products), with evidence-extraction techniques — dependency analysis, git-history mining — applied to the source estate before a single mapping is trusted.

Travel may be required for this role. The amount of travel will vary from 0 to 100% depending on business need and client requirements.

Here's what you need:

Minimum of 5 years of hands-on data engineering or data platform engineering, with at least 1–2 years specifically on AI/LLM-related data work.

Minimum of 4 years of experience working in agile engineering or consulting teams.

Minimum of 3 years with Python and SQL at production scale, plus at least one cloud data platform.

Working fluency with data lineage and data observability — catching drift as data drift before it surfaces as AI drift Minimum of 1 year designing data supply for AI systems — vector stores, embedding pipelines, or agent-facing data products.

AI-adjacent experience counts toward the bars above: strong credentials in the neighboring discipline combined with 1–2 years of hands-on LLM work is exactly the profile we grow.

Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate's Degree, must have minimum 6 years work experience).Bonus Points if You Have:

Streaming and CDC experience (Kafka, Flink, or equivalent)
Code-analysis or archaeology tooling exposure — you'll work beside our excavation teams.

Agentic data architecture: designing how data is supplied to multiple cooperating agents.

Compensation at Accenture varies depending on a wide array of…