Director, Data Engineering

Hanwha Energy USA•Houston, TX

About The Position

Hanwha Energy USA, headquartered in Houston, Texas, is part of the Hanwha Group—a FORTUNE Global 300 company and one of South Korea’s most respected business enterprises. With over a decade of experience delivering high-quality, utility-scale energy projects across North America, Hanwha Energy USA has evolved into a comprehensive energy solutions provider. Our portfolio now spans utility-scale renewables, natural gas generation, retail electricity, and strategic partnerships that power America’s growing data center industry. Our expertise covers the entire energy value chain—from project development and engineering to construction, operations, and maintenance. By integrating advanced technologies, proven processes, and strong partnerships, we deliver reliable, customized solutions that meet the dynamic needs of local energy markets. Hanwha Energy USA is actively advancing strategic initiatives in natural gas generation and data center development, including hyperscaler solutions on both sides of the meter. We are proud to serve as the parent company of: Hanwha Renewables – specializing in utility-scale solar and battery energy storage systems (BESS) Chariot Energy – providing retail electricity services for residential, commercial, and industrial customers in deregulated markets. We are seeking a Director of Data Engineering to lead the strategy, architecture, and delivery of a modern enterprise data platform. This leader will own and actively contribute to the enterprise data strategy and backlog, ensuring that data engineering, reporting, and analytics are coordinated under strong technical leadership and built on a governed, reliable, and scalable foundation. This is a hands-on leadership role for a player-coach who can operate at both the strategic and technical levels. The ideal candidate will go well beyond traditional SQL Server administration and reporting support. This person will help modernize our data environment by introducing the right technologies, patterns, and operating discipline across areas such as cloud data platforms, data lakes, streaming/event-driven data, data quality, lineage, semantic modeling, and AI-ready data architecture. This role is critical to ensuring that AI solutions, business reporting, and operational decision-making are powered by trusted, well-managed data with measurable business impact. The employee may be required to perform other job-related duties as requested by management. All duties will be assigned in accordance with applicable laws and company policies.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Engineering, Mathematics, or a related field. Equivalent experience may be considered.
15+ years of progressive experience in data engineering, analytics engineering, data architecture, or enterprise data platform development.
5+ years of leadership experience managing technical data teams.
Proven experience designing and delivering modern data pipelines and analytics platforms in a business environment with multiple stakeholders.
Strong experience with SQL and relational data platforms, but also a demonstrated ability to move beyond legacy-only approaches into modern data architecture.
Experience with cloud data platforms, data lakes, and scalable data pipeline design.
Experience with streaming, event-driven, or near-real-time data integration patterns.
Experience establishing standards for data quality, lineage, documentation, and governance.
Strong understanding of how data platforms support BI, reporting, analytics, and AI use cases.
Experience leading a mix of full-time employees, contractors, and/or vendor resources.
Ability to work in the details while also setting strategic direction.

Nice To Haves

Experience with Azure-based data services and modern cloud-native data patterns.
Experience with lakehouse or data lake architectures.
Experience with streaming technologies and message/event-based integration patterns.
Experience supporting AI and machine learning use cases with well-structured, governed data.
Experience with semantic models, dimensional modeling, and performance optimization for analytics.
Experience in operationally complex or multi-entity environments.
Experience modernizing legacy reporting/data stacks into more scalable enterprise platforms.

Responsibilities

Own and actively contribute to the enterprise data strategy and roadmap.
Define and prioritize the data engineering backlog, balancing foundational work, new business needs, AI enablement, reporting, and technical debt reduction.
Design and oversee key data models, pipelines, integration patterns, and analytics platforms that support AI, BI, operational reporting, and decision-making.
Lead data engineering and reporting teams including full-time employees, contractors, and vendors.
Stay close to the work by reviewing pipeline designs, resolving complex data issues, and guiding architecture and implementation decisions.
Establish practical standards for data quality, lineage, documentation, observability, governance, and operational support.
Drive the evolution of the company’s data environment beyond basic SQL Server patterns into modern capabilities such as: cloud-based data platforms, data lakes / lakehouse architectures, streaming and near-real-time data pipelines, scalable ELT/ETL frameworks, governed semantic and analytics layers.
Partner with business stakeholders, application development teams, AI engineers, analysts, and leadership to ensure data platforms meet current and future needs.
Ensure data structures and delivery patterns support both business intelligence and AI use cases, including trusted source data, feature-ready datasets, retrieval-ready content, and performance-optimized views.
Evaluate and introduce appropriate new technologies, tools, and design patterns that improve scalability, maintainability, speed, governance, and cost efficiency.
Oversee data integration across enterprise systems such as ERP, CRM, project platforms, contract repositories, ticketing systems, and internally developed applications.
Collaborate with infrastructure, security, and application teams to ensure data solutions align with enterprise architecture, security controls, identity standards, and cloud strategy.
Build strong operating rhythms around prioritization, delivery, quality review, issue resolution, and stakeholder communication.
Mentor senior engineers and analysts while raising the technical maturity of the broader team.