Data Modeler/Architect

Accenture•Houston, TX

2d•Hybrid

About The Position

Accenture is investing $3B in Data & AI to help clients optimize and reinvent their businesses. The Data & AI organization comprises over 45,000 professionals and focuses on experienced innovation, strategic investment, exceptional talent, and ecosystem power. This role is for a strong Data Modeler/Data Architect Lead who will provide technical leadership, architectural direction, and engineering governance for enterprise data platform initiatives. This is not a hands-on-only developer role; the ideal candidate will guide the team on architecture, data engineering best practices, design patterns, governance, data quality, and scalable implementation. The candidate should be an independent thinker capable of evaluating requirements, challenging designs, influencing stakeholders, and ensuring sound data engineering principles are adopted.

Requirements

6+ years of dedicated data modeling experience within enterprise environments, focusing heavily on conceptual and logical design in complex industrial, supply chain, or energy domains.
Proven track record of working directly with business stakeholders (analysts, data scientists, quants, risk professionals) to gather requirements.
Proven track record of partnering effectively with Product Owners and Architects to execute technical roadmaps.
Mastery of data modeling methodologies (Dimensional Modeling/Kimball, 3NF/Relational).
Proficiency with industry-standard modeling tools (e.g., ERwin, PowerDesigner, or modern visual/code-based modeling frameworks).
Strong conceptual understanding of physical supply chains, time-series data relationships, spatial/geographical hierarchies, and market fundamental data.
Familiarity with modern cloud data platform concepts and Medallion Architecture patterns (separating raw, conformed, and dimensional layers logically).
Familiarity with modern code-based data modeling and documentation frameworks (such as dbt-native modeling/tests).
Prior exposure to energy market fundamental data feeds (e.g., vessel tracking, EIA reports, production statistics).
Experience in Agile/Scrum delivery environments, working alongside Product Owners in feature grooming and backlog planning.

Nice To Haves

Experience with Unity Catalog, Databricks Workflows, Delta Live Tables / Lakeflow, Auto Loader, Change Data Feed, and materialized views.
Experience with cloud platforms such as Azure, AWS, or GCP.
Bachelor’s or Master’s degree in Computer Science, Data Management, Information Systems, Industrial Engineering, or a related quantitative field.

Responsibilities

Spearhead the conceptual and logical data design for the Commercial Energy Market Fundamentals and Supply Chain analytics platform.
Act as a bridge between business vision and technical execution.
Work with stakeholders (Data Scientists, Risk Managers, Market Analytics teams) to uncover analytical needs, conduct data-scoping workshops, and translate complex physical energy concepts into rigorous data models.
Engage with Product Owners and Data Architect to guide backlog prioritization, define structural requirements, and oversee implementation across a Medallion Architecture.
Architect enterprise-grade taxonomies, conformed dimensions, and hierarchical structures for core energy domains: Market Balances, Logistics & Spatial Data, Asset Frameworks, and Market Curves & Pricing.
Translate business concepts and domain rules into formal conceptual entity-relationship diagrams (ERDs) and logical data models (LDMs), establishing enterprise-wide data standards.
Collaborate with Product Owners to shape the data product roadmap and with Data Architects to ensure logical models align with enterprise target-state architecture and implementation standards.
Define structural contracts and semantic transformations for data movement within a Medallion Architecture (raw, conformed Silver layer, business-optimized Gold layer).
Establish enterprise data modeling standards, naming conventions, and best practices.
Partner with data governance teams to maintain the enterprise data dictionary and end-to-end logical lineage.