Enterprise Data Architect Consultant

Job Description

Job Description

Enterprise Data Architect Consultant

Position Summary
CG Infinity is seeking a strategic and hands-on Enterprise Data Architect to design, build, and lead the implementation of a scalable, enterprise-wide data Lakehouse. This role will be responsible for developing a modern data architecture from the ground up, integrating multiple systems and business units into a unified data ecosystem that drives analytics, reporting, and business decision-making.
The ideal candidate combines deep technical expertise with strong business acumen, capable of leading discovery efforts, defining priorities, and ensuring data solutions are aligned with organizational goals. This individual will also play a key leadership role in establishing data governance, master data management (MDM), and enterprise data standards.

Key Responsibilities
Enterprise Data Architecture & Strategy

• Design and implement a scalable, secure, and high-performance enterprise data Lakehouse from inception.
• Define the end-to-end data architecture, including data ingestion, transformation, storage, integration, and consumption layers.
• Establish architectural standards, frameworks, and best practices aligned with business and technology strategies.
• Evaluate and recommend technologies (cloud platforms, ETL/ELT tools, data lakes, Lakehouses) to support long-term scalability.

Cross-Functional Discovery & Requirements Alignment

• Lead discovery sessions with business and technical stakeholders to identify high-value use cases, priorities, and dependencies.
• Translate business requirements into technical data models, data flows, and architecture designs.
• Ensure alignment between data solutions and business objectives, including KPIs, reporting, and analytics needs.
• Develop and maintain a data roadmap with clearly defined phases, milestones, and deliverables.

Data Lakehouse Development & Delivery

• Oversee development of integrated data pipelines that connect disparate systems (ERP, CRM, operational systems, third-party platforms).
• Define and implement data models (conceptual, logical, physical) to support analytics and reporting.
• Establish data quality frameworks and ensure reliability, consistency, and integrity of enterprise data.
• Ensure performance optimization and scalability of the data environment.

Data Governance & Master Data Management

• Develop and implement enterprise data governance policies, standards, and controls.
• Lead Master Data Management (MDM) initiatives to standardize key business entities across systems.
• Define data ownership, stewardship, and accountability models across business units.
• Ensure compliance with regulatory, security, and data privacy requirements.

Leadership & Stakeholder Engagement

• Act as a trusted advisor to executive leadership, including the CTO and business leaders.
• Communicate complex technical concepts clearly to non-technical stakeholders.
• Lead cross-functional teams, including data engineers, analysts, and business users.
• Drive adoption of data solutions across the organization through change management and stakeholder alignment.

Required Qualifications

• 8+ years of experience in data architecture, data engineering, or enterprise data management roles.
• Proven experience building an enterprise data Lakehouse from scratch spanning multiple systems and business units.
• Strong experience with data modeling, ETL/ELT design, and data integration frameworks.
• Hands-on experience with cloud data platforms (e.g., Azure, AWS, or GCP).
• Demonstrated expertise in:
  • Python Development
  • Data Governance frameworks
  • Master Data Management (MDM)
  • Data quality and metadata management
• Experience leading discovery sessions and requirements gathering workshops with senior stakeholders.
• Strong understanding of enterprise systems (ERP, CRM, operational apps) and integration patterns.
• Excellent communication, facilitation, and leadership skills.

Preferred Qualifications

• Experience in consulting environments or multi-client, multi-business unit organizations.
• Industry experience in oil & gas or chemical sectors, with an understanding of upstream, midstream, downstream, or refining operations.
• Familiarity with modern data tools (e.g., Snowflake, Databricks, Azure Synapse, Power BI, Tableau).
• Experience implementing data lakes, Lakehouse architectures, or hybrid data ecosystems.
• Knowledge of Agile and iterative delivery methodologies.
• Relevant certifications (e.g., Azure Data Architect, AWS Data Analytics, DAMA CDMP).

Success Metrics

• Successful delivery of a fully operational enterprise data Lakehouse aligned with business priorities.
• Measurable improvement in data accessibility, quality, and reporting capabilities.
• Adoption of data governance and MDM practices across business units.
• Delivery of a clear project roadmap with defined milestones, timelines, and outcomes.
• Positive stakeholder feedback on alignment between business needs and technical solutions.

 

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