Data Architect Sr. Manager

Accenture•Chicago, IL

1d•Hybrid

About The Position

We Are: The beginning of a new Data & AI decade that will reshape work and society has begun. Accenture is stepping boldly into this future with a clear strategy and purpose: to help clients optimize and reinvent their business with data & AI — backed by a $3B investment and commitment to our people to do industry-defining work. With over 45,000 professionals dedicated to Data & AI, Accenture’s Data & AI organization brings together our Experienced Innovation, Strategic Investment, Exceptional Talent, and Power Ecosystem. You Are: You are a data/senior data architect who designs enterprise data fabric solutions that provide unified, intelligent, automated access to distributed data across on-prem and multi-cloud environments. You bring together data virtualization, active metadata, knowledge graphs, cataloging, and automated integration into a connected data layer that powers analytics and AI — and you can define the reference architectures and integration patterns that make it real.

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
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
Experience working in Energy, Manufacturing, Utilities or similar industries

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.