Enterprise Data Architect Consultant

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 develop 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.

Responsibilities

• 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
• 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
• 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
• 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
• 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

Requirements

• 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