Principal Architect Data Platforms

Beusa Energy, LLC•The Woodlands, TX

1d•Onsite

About The Position

The Principal Architect – Data Platforms is responsible for the end‑to-end architecture, design, and technical integrity of the enterprise data platform, including the data lakehouse, data pipelines, and semantic/analytics layers. This role sets the technical patterns and standards for how data is ingested, modeled, transformed, and served for analytics, reporting, and AI/ML use cases across the BEUSA family of companies. The Principal Architect – Data Platforms will serve as the technical authority for the data platform, providing architectural direction to Data Engineers, BI Developers, and related teams, while partnering closely with the Data Governance & Delivery Manager, Data Engineering Supervisor, and AI/ML Engineering Manager. The role is hands‑on enough to produce reference implementations and design patterns, but primarily focused on system‑level design, standards, and cross-team alignment. The ideal candidate is an experienced data architect with deep expertise in modern data platforms (e.g., Databricks, lakehouse architectures), strong data modeling skills, and a track record of designing scalable, resilient data ecosystems that support advanced analytics and AI.

Requirements

Successfully passes all applicable general pre-employment testing including but not limited to: background check, pre-employment drug screening, pre-employment fit tests, pre-employment aptitude and/or competency assessment(s).
Valid U.S. Driver’s License required.
Employment is contingent upon meeting the company's driving standards, including an acceptable Motor Vehicle Record (MVR) in accordance with Company policy.
Daily in-person, predictable attendance.
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, Engineering, Mathematics, or a related field required.
8+ years of professional experience in data engineering, data architecture, or a related field.
3+ years in a lead/principal architect or equivalent senior role, designing and governing modern data platforms.
Demonstrated experience in dimensional and/or domain-driven data modeling for analytics and BI.
Experience with a modern data engineering tool stack (Databricks, Snowflake, dbt, etc.).
Experience working with BI/analytics tools and semantic layers (e.g., Sigma, Power BI, etc.).
Technical Expertise: Deep understanding of modern data platform architectures, including lakehouse, data warehousing, and streaming/batch data processing.
Strong expertise with Databricks or similar platforms (e.g., Spark, Delta Lake, Infrastructure as Code, DAG job orchestration).
Proficiency in SQL and at least one programming language commonly used for data engineering (e.g., Python, Scala).
Strong data modeling skills (relational, dimensional, and/or data vault; familiarity with domain‑driven design concepts is a plus).
Experience designing and governing data pipelines, ETL/ELT workflows, and data products at scale.
Familiarity with BI semantic modeling and metric standardization.
Experience with cloud environments (e.g., AWS, Azure, or GCP) for data workloads, including storage, compute, security, and monitoring.
Understanding of data governance, data quality frameworks, and a strong desire for excellence in metadata management.
Architecture & Leadership Skills Proven ability to define and drive adoption of architecture standards, reference patterns, and best practices across multiple teams.
Strong track record of making clear, principled technical decisions and standing up those decisions under pressure.
Experience influencing and aligning cross‑functional stakeholders (Data Engineering, BI, AI/ML, IT, Security, Product/Business).
Ability to conduct effective design and code reviews, and to coach engineers and analysts toward better solutions.
Business & Communication Skills: Excellent verbal and written communication skills, with the ability to present technical topics to both technical and non-technical audiences.
Proven ability to work independently, manage multiple priorities, and deliver results in a fast-paced environment.
Demonstrated ability to mentor and provide technical guidance to junior team members.
Curiosity & Growth Mindset: A high degree of curiosity, with the ability and desire to learn new skills both on-the-fly and in formal learning environments.
Openness to feedback and a continuous improvement mindset for both technology and processes

Nice To Haves

Experience in environments with advanced analytics and/or AI/ML workloads strongly preferred.
Oil & Gas industry experience is a plus.

Responsibilities

Data Platform Architecture & Strategy Define and own the target architecture for the enterprise data platform, including lakehouse design, data domains, medallion architecture, and serving patterns for analytics and AI/ML.
Develop and maintain the roadmap for evolving the data platform in alignment with business strategy and technology standards.
Standards, Patterns, and Best Practices Establish and document standards, reference architectures, and reusable patterns for data ingestion, transformation, storage, consumption, access/security, quality, and observability.
Ensure consistent use of patterns and standards by Data Engineering and BI teams through design reviews and technical governance.
Design efficient data models to support multiple use cases (BI, reporting, AI/ML model serving, feature stores, etc.)
Technical Authority for Data Engineering & BI Provide architectural guidance and decision‑making for complex data engineering and BI initiatives, including schema design, data contracts, and performance optimization.
Serve as the final technical decision‑maker on data platform design questions, after consultation with the Data Engineering Supervisor and BI leads.
Ensure that Data Engineering and BI teams, regardless of administrative reporting lines, adhere to data platform standards, patterns, and data contracts.
Collaboration with Governance & Delivery Partner with the Data Governance & Delivery Manager to ensure data architecture aligns with governance policies, data quality requirements, and regulatory needs.
Translate governance and domain requirements into concrete technical standards and implementation patterns.
AI/Analytics Enablement Collaborate with the AI/ML Engineering Manager and AI Product Lead to ensure the data platform effectively supports AI, ML, and advanced analytics use cases.
Design data products, feature stores, and analytical data sets that are reliable, well‑modeled, and optimized for downstream AI/ML workloads.
Integration with Applications & Enterprise Architecture Partner with the Principal Architect – Applications Engineering to design robust data integrations, APIs, and event flows between operational systems and the data platform.
Define and enforce data contracts and event schemas for systems that act as sources of record.
Hands‑On Reference Implementation May build or co‑build key reference pipelines, models, and data products to demonstrate best practices and accelerate adoption of standards.
Conduct design and code reviews for critical or strategic data initiatives.
Performance, Reliability, and Cost Optimization Define and oversee standards for data platform performance, scalability, reliability, and cost efficiency.
Partner with platform/IT teams to optimize infrastructure usage and monitor platform health.
Technical Mentorship & Knowledge Sharing Provide technical mentorship to Data Engineers, BI Developers, and other team members, helping raise the overall technical bar.
Lead architecture reviews, design sessions, and internal training on data platform patterns and tools.
Stakeholder Engagement Collaborate with business stakeholders, IT, Security, and other partners to understand requirements and ensure the data platform supports current and future needs.
Communicate complex architectural concepts to both technical and non‑technical audiences.
Continuous Improvement & Innovation Stay current on advancements in data platform technologies (e.g., Databricks, lakehouse architectures, streaming, metadata management) and recommend appropriate adoption.
Continuously refine architecture, standards, and tooling based on lessons learned and evolving business priorities.
Performs other related duties as assigned to assist with successful operations and business continuity.