Data and AI Architect

ConocoPhillips•Houston, TX

About The Position

ConocoPhillips is seeking a highly skilled and experienced Data and AI Architect to lead the design and development of Digital & AI Solutions aligned with the company's digital strategy. This role focuses on integrating foundational data ecosystem components with cutting-edge Generative Artificial Intelligence and Model Context Protocol (MCP) to enhance data quality for business processes and analytics. The architect will be key in advancing AI capabilities, integrating with Data Lake and Data Warehouse functionalities, and shaping the strategic and operational aspects of AI product development and management. As part of the data architect team, this role will implement data projects, enforce data management standards, and develop technology solutions for various data types, including data catalogs and lineage. The Data and AI Architect will significantly influence the company's journey towards a strategic Data and AI organization, requiring strong technology focus, hands-on design/architecture experience, digital/cloud technology integration expertise, and excellent communication skills.

Requirements

Legally authorized to work in the United States
Bachelor's degree or higher in Computer Science, Engineering, Geoscience, Information Technology, Information Systems, related field or foreign equivalent
7 or more years of experience in data architecture, and data management in an enterprise environment
1 or more years of experience with design and enablement of enterprise AI
1 or more years of experience with implementation of GenAI based solutions (Agentic workflows)
1 or more years of experience using the AI Assistants (like Microsoft copilot cowork, Anthropic claude code, Snowflake CoCo) for product/solution development
Intermediate level of knowledge and experience designing and implementing solutions using MCP technologies

Nice To Haves

7 or more years of experience with Cloud Platforms on Data Science and Analytics
7 or more years of experience implementing and supporting complex Data Management initiatives e.g., Snowflake, ADLS, Databricks
5 or more years of experience with database platforms such as Snowflake or Teradata with demonstrated strength in SQL, data modeling, ETL development, and data warehousing
5 or more years of Energy industry experience
Hands on experience and proficiency in Data Management tools – SQL, SDE, JSON, XML, scripting languages such as Python
Big Data technology stack including NoSQL, Spark, Hive, Kafka, StreamSets, IICS, ADF etc
Experience with Spark and any of the following programming languages: Python, Scala, Java etc
Hands on experience designing and implementing data ingestion techniques for real-time and batch processes into cloud based platforms
Delta Lake experience with query optimization
Work across structured, semi-structured, and unstructured data, with strong technical experience in large distributed systems, data warehousing, data lake at scale
Deep subject matter expertise and hands on experience in designing, scaling and implementing data lake platform
Advanced level of knowledge of master data management, real-time streaming data and cloud technologies
Advanced knowledge of well data lifecycle from exploration through to development, drilling and completion, production and eventual abandonment or relinquishment
Advanced level of proficiency in DevOps , including Agile development process, containers, and source code control systems
Intermediate level of knowledge of Data Management principles, processes and data lifecycle
Understanding of Data Science and related technologies
Ability to take ownership, engage, lead change, achieve results, adapt, problem solve, manage risk and drive tasks to completion
Ability to lead initiatives, multi-disciplinary project teams, and influence stakeholder buy-in
Participate in strategy and planning sessions and be recognized as a capable team member that delivers value
Demonstrated ability to deal with ambiguity and maintain effective performance under stressful and uncertain conditions
Excellent oral and written communication skills; proven ability to represent point of view and discuss technical information with the business users in business language
Team player, and self-driven individual who can multi-task, work independently under minimal supervision and deliver on commitments
Excellent analytical mind and proven problem-solving skills
Demonstrated leadership skills, with ability to engage at all levels of the organization
Confident in engaging all levels of the organization in communicating a data strategy
Ability to influence others and drive change around data standards and best practices

Responsibilities

Identifying and evaluating the latest Artificial Intelligence (AI) capabilities, Large Language Models (LLMs) and technologies.
Integrating AI Models with applications using Model Context Protocol (MCP) and other cutting-edge capabilities to drive business results and innovation.
Defining end-to-end AI solution architecture that integrates with the established data foundation to deliver insights and predictions.
Establishing strategies, design patterns, guardrails, decision matrices, information models and frameworks that drive operational efficiencies across the AI landscape.
Identifying gaps and opportunities in the AI landscape and formulate a roadmap for enhancements.
Assessing the latest LLMs and enabling them for use cases.
Operationalizing new AI capabilities, LLMs and tools, ensuring seamless integration with existing systems and processes (e.g. security, scalability, performance, reliability, FinOps, recovery etc.).
Collaborating with the Responsible AI committee to establish Responsible AI and AI-Ops (LLM-Ops) practices across the organization.
Providing technical leadership and mentorship to development teams on implementing AI technologies and patterns.
Defining and operationalizing AI optimization techniques to address performance, cost and efficiency.
Creating enterprise blueprints that tie together business processes and workflows with data to strengthen AI capabilities, accelerate delivery, and promote innovation.
Cultivating relationships with peer Data Management groups and Enterprise Architects to ensure alignment, compliance and long-term sustainability of digital assets.
Consulting and/or participating in risk review process and Architecture Review board to ensure AI projects/tools/models align with the organization's risk management and architecture standards.
Ensuring consistency of AI models & tools, and architectural best practices across the organization, driving adoption across teams and systems.
Proactively identifying opportunities for automation and continuous improvements.
Staying current with the latest trends, technologies, and best practices in the AI stack - management, governance, and architecture.
Participating and supporting Agile quarterly planning for the team.
Establishing AI capabilities to seamlessly complement the Data Lake, Data Warehouse, forming a unified and holistic platform to meet all data requirements.
Ensuring consistency of AI models, tools, and architectural best practices across the organization, driving adoption across our teams and systems.
Establishing synergies and decision trees to map the right AI tools for the use cases.
Supporting other business groups (such as Asset & Operations Integrity, Construction, Damage Prevention, etc.) with pipeline related activity.