Director-Software Engineering-Unify Energy

Roles and Responsibilities

• Lead, mentor, and develop high-performing engineering teams responsible for the Unify Energy platform and related software, data, integration, and automation capabilities.
• Own the engineering strategy, technical roadmap, execution plan, and delivery outcomes for Unify Energy in alignment with product strategy, customer commitments, revenue goals, and long-term platform direction.
• Build strong working partnerships with oil and gas business leaders, customers, Product Management, Implementation, and Customer Success to understand industry workflows, prioritize the highest-value problems, and convert business outcomes into clear engineering plans.
• Provide technical leadership across SaaS software engineering, cloud-native architecture, APIs and integrations, data pipelines, search and document services, analytics, workflow automation, and applied AI.
• Oversee the full software development lifecycle, from discovery and architecture through development, testing, deployment, monitoring, support, and continuous improvement.
• Drive delivery of scalable, secure, and maintainable capabilities that support energy information ingestion, normalization, indexing, classification, validation, search, retrieval, workflow, and reporting.
• Advance platform capabilities that enable Access to serve as a trusted steward of customers’ physical and digital information assets by supporting metadata management, intuitive search, filtering, navigation, and map-based discovery within the SaaS platform. Lead the data engineering capabilities required to ingest, organize, and enrich customer data so it becomes more accessible, useful, and valuable across oil and gas workflows.
• Guide integration strategy for industry and enterprise systems, including data exchanges, APIs, batch interfaces, identity services, content repositories, GIS or spatial data, analytics platforms, and customer-specific systems of record.
• Lead data engineering and data quality practices for high-volume structured and unstructured information, including ETL/ELT, metadata management, lineage, reconciliation, observability, and reliable downstream consumption.
• Guide responsible use of AI and machine learning to improve document understanding, metadata extraction, classification, entity matching, search relevance, workflow recommendations, quality assurance, and operational efficiency.