Data and Analytics Platform Architect

Motiva Enterprises LLC•Houston, TX

About The Position

We are seeking a hands-on Data and Analytics Platform Architect to lead the evolution of our enterprise data integration and analytics platform. This role focuses on building a robust, scalable data ecosystem on Azure and Databricks to support diverse business use cases—from traditional BI reporting to advanced analytics and machine learning. You will refine our medallion (multi-layer) data pipelines, implement a data mesh layer across multiple data repositories, and ensure seamless, high-quality data flows. The Architect is also expected to explore emerging tools and technologies (including AI/LLM integration where appropriate) to enhance the platform’s capabilities, efficiency, and cost-effectiveness. Strong collaboration skills and a commitment to delivering incremental business value are essential.

Requirements

Bachelor’s degree in Computer Science, Data Engineering, Data Science, or a related field.
10+ years of experience designing and evolving large-scale data analytics platforms, with deep expertise in data integration (ETL/ELT), medallion-tier data pipelines, cloud data services, and MLOps.
Hands-on experience with Microsoft Azure (required) and big data processing (Spark/Databricks).
Strong familiarity with Delta Lake, data lakehouse architecture, and applying data mesh principles.
Proven experience with at least two of: Azure Data Services (e.g., Synapse, Data Lake, Data Factory), Databricks, SAP Datasphere, or Cognite Data Fusion (CDF).
Proven experience in building analytical data models (including STAR schema etc.)
Solid understanding of enterprise data governance, security (access controls, data privacy), and data quality best practices.
Demonstrated success leading enterprise-wide data initiatives end-to-end.
Excellent problem-solving and analytical abilities.
Strong communication and interpersonal skills; able to work effectively with both technical and non-technical stakeholders.
Ability to work on solutions for multiple projects and priorities in a fast-paced environment.

Nice To Haves

Experience integrating AI/ML or LLM solutions into data platforms (e.g., using Azure Cognitive Services/OpenAI, implementing LLM gateways for multi-provider model integration, or leveraging context frameworks like MCP for AI-driven data products).
Master’s degree in a relevant field.
Industry experience in Oil & Gas (Downstream) or other large-scale industrial analytics environments.

Responsibilities

Solution Design: Work closely with business and IT stakeholders to understand data & analytics needs and translate them into scalable solutions.
Partner with data engineers, data scientists, and analysts to ensure platform capabilities align with requirements and enable successful delivery of data products and insights.
Implement policy enforcement points and access controls (e.g., data catalogs, encryption, RBAC) across the platform to ensure compliance, privacy, and data protection.
Documentation & Enablement: Create and maintain clear documentation for data architecture, integration processes, and platform best practices colloborating with Enterprise Architecture team .
Share knowledge and mentor technical teams to promote effective use of the data platform.
Analytical Data Modeling & Schema Design: Design, build, and evolve dimensional data models, including star and snowflake schemas, optimized for analytics and reporting.
Balance data model simplicity, flexibility, and performance while minimizing complexity and redundancy across analytical datasets.
Develop and refine medallion architecture (bronze-silver-gold layers) for efficient data ingestion, transformation, and consumption across the organization.
Optimize Data Pipelines: Ensure ETL/ELT processes, data pipelines, and tools (e.g., Azure Data Factory, Databricks/Spark jobs) run efficiently to deliver timely, high-quality data for analytics, AI/ML, and business intelligence.
Identify and resolve data workflow bottlenecks, continuously monitoring and tuning for performance and scalability.
Integrate AI & Agentic Capabilities: Incorporate AI and agentic (autonomous agent) capabilities as complementary parts of the data platform.
Design and evolve secure LLM integration patterns (using enterprise LLM gateways to centralize model access, routing, and governance with cost controls) and leverage frameworks like Model Context Protocol (MCP) to connect AI applications/agents with enterprise data sources in a secure, governed manner—enabling intelligent, agent-driven data workflows.