Data Engineering Manager - AI & Data Modernization
$94.4k - $293.8kAccenture Global
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 at scale. Through 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 accenture.com. We 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 resilient, trusted data foundations to power AI at scale — the pipelines, lakehouse substrate, and data-plane governance that let an enterprise trust what flows into its models and agents. You Are: A data engineer who thinks 'governed' and 'fast' are the same requirement stated twice. 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 modernization engagement: batch and streaming pipelines, productized data ready for AI consumption, and the data-plane governance — catalog, quality, lineage, observability — that downstream trust stands on 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 5 years of experience working in agile engineering or consulting teams.
- Minimum of 5 years with Python and SQL at production scale, plus at least one cloud data platform.
- Minimum of 5 years with streaming technologies and orchestration tooling (Kafka, Airflow, or equivalent).
- Minimum of 1 year designing for both AI workloads and classical analytics on the same foundation.
- 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).
- Lakehouse/open-table-format depth (Delta, Iceberg).
- Data-mesh experience in decentralized organizations.
- Vector and graph stores alongside the relational stack.
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