Data Architecture/Data Fabric Senior Manager - Utilities

Accenture•Chicago, IL

21h•Hybrid

About The Position

Accenture's AI and Data practice sits at the intersection of deep industry knowledge and applied AI and data engineering. We help the world’s leading Resources and Utilities organizations reinvent how they run — designing the data foundations, AI platforms, and governance models that turn data into trusted, production-grade intelligence. We are not looking for generalists who advise on AI in the abstract. We need practitioners who understand how enterprises actually operate, where the friction lives, and how to engineer smarter solutions using AI and data to fundamentally transform business processes and outcomes.

Requirements

Minimum of 10 years of experience in data architecture or enterprise data engineering
Minimum of 6 years of experience architecting data fabric solutions (data virtualization, active metadata, knowledge graphs, automated integration)
Minimum of 5 years of experience with cloud data platforms (e.g., Snowflake, Databricks, Azure Synapse, BigQuery)
Minimum of 4 years of experience with data integration/ETL/ELT and metadata & lineage tooling
Minimum of 3 years of experience with API and streaming architectures
Minimum of 1 year of experience serving utilities clients (electric, gas, or water) or in a utilities finance, controllership, or regulatory function.
Bachelor's degree or equivalent (minimum 12 years' work experience). If Associate’s Degree, must have equivalent minimum 6-year work experience.

Nice To Haves

Master’s degree in a relevant field
Hands-on experience applying data mesh and data product operating models
Cloud or data platform architecture certifications

Responsibilities

Architect enterprise data fabric — design data fabric solutions that deliver unified, intelligent, automated access to distributed data across on-prem and multi-cloud environments.
Integrate the connected data layer — design the integration of data virtualization, active metadata, knowledge graphs, cataloging, and automated data integration into a connected data layer for analytics and AI.
Define reference architectures — establish reference architectures, integration patterns, and governance integration that scale across the enterprise.
Enable data products and self-service — support self-service consumption, data products, and real-time data delivery through the fabric.
Apply modern data paradigms — bring data mesh and data product concepts, cloud data platforms (Snowflake, Databricks, Azure Synapse, BigQuery), and streaming/API architectures to bear on client problems.
Advise and shape solutions — engage client architects and leaders as a trusted advisor and shape solutions for major data modernization pursuits.