Technical Lead, Data Integration, Warehousing & Analytics

KBR, Inc.•Chantilly, VA

About The Position

The Technical Lead, Data Integration, Warehousing & Analytics serves as the primary technical expert and hands-on leader responsible for the design, development, and optimization of enterprise data integration pipelines, data warehouse platforms, and analytics solutions. This role bridges operational data sources and business intelligence capabilities by architecting scalable data solutions that enable trusted, actionable insights across the organization. The ideal candidate combines deep expertise in data engineering, data warehousing, ETL/ELT development, and analytics platforms with strong technical leadership and mentoring capabilities. This individual partners closely with business stakeholders, data consumers, and IT leadership to deliver modern data solutions while promoting data quality, governance, scalability, and self-service analytics capabilities.

Requirements

Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related field required; Master's degree preferred.
6+ years of progressive experience in data engineering, data integration, data warehousing, ETL/ELT development, and business intelligence.
Experience serving as a technical lead, senior engineer, or senior individual contributor on complex data and analytics initiatives.
Experience designing and supporting enterprise-scale data platforms and reporting solutions.
Strong hands-on expertise with Oracle databases, advanced SQL, and enterprise data modeling techniques.
Experience designing, developing, and supporting ETL/ELT solutions using Informatica or modern data integration platforms such as dbt, Azure Data Factory, or similar technologies.
Strong understanding of dimensional and normalized data modeling methodologies.
Experience supporting enterprise data warehouse and data lake architectures.
Experience with Power BI or comparable analytics and business intelligence platforms.
Experience with workload automation and job scheduling technologies such as UC4/Automic, Control-M, Autosys, or similar platforms.
Understanding of data quality, master data management (MDM), metadata management, and data governance best practices.
Strong technical problem-solving and analytical skills.
Ability to communicate complex technical concepts effectively to both technical and non-technical stakeholders.
Strong stakeholder management and collaboration skills.
Experience leading technical design discussions, architecture reviews, and solution planning activities.
Demonstrated ability to mentor team members and promote technical excellence without direct people-management responsibility.

Nice To Haves

Experience with cloud-based data platforms and services, including Microsoft Azure, AWS, or Google Cloud Platform (GCP).
Experience with modern data lakehouse architectures and cloud-native analytics solutions.
Familiarity with data catalog, lineage, and governance tools.
Experience implementing self-service analytics and enterprise reporting strategies.
Experience supporting large-scale data modernization or cloud migration initiatives.
Knowledge of AI, machine learning, or advanced analytics platforms and their integration into enterprise data ecosystems.
Relevant certifications in cloud platforms, data engineering, business intelligence, or analytics technologies.

Responsibilities

Lead the technical design and implementation of scalable, end-to-end data integration pipelines utilizing modern ETL/ELT frameworks and best practices.
Architect and oversee the ingestion of data from enterprise applications, databases, APIs, and third-party systems into centralized data platforms.
Ensure data integration solutions are reliable, performant, scalable, and well-governed through robust error handling, monitoring, and automation.
Establish and enforce technical standards for integrations, APIs, data contracts, pipeline orchestration, and development methodologies.
Drive continuous improvement initiatives that enhance data availability, integration efficiency, and operational reliability.
Architect, implement, and support enterprise data warehouse and data lake environments.
Design and maintain logical and physical data models, storage strategies, partitioning structures, and performance optimization frameworks.
Champion best practices for data modeling, metadata management, database administration, and data lifecycle management.
Collaborate with data owners and business stakeholders to support data governance, master data management (MDM), and data quality initiatives.
Ensure enterprise data platforms remain scalable, secure, maintainable, and aligned with business requirements.
Translate complex business requirements into scalable analytics solutions, data models, and actionable insights.
Define and maintain technical standards and roadmap initiatives for business intelligence and analytics platforms, including Power BI and related technologies.
Enable governed self-service analytics through the development of dashboards, reports, semantic models, and reporting frameworks.
Partner with stakeholders to define key performance indicators (KPIs), measurement frameworks, and reporting strategies.
Promote data literacy and encourage data-driven decision-making across the organization.
Provide technical mentorship, code reviews, and architectural guidance to data engineers, ETL developers, and BI analysts.
Lead technical design sessions, proof-of-concept initiatives, architecture reviews, and platform evaluations.
Partner with business stakeholders and IT leadership to translate strategic objectives into scalable data solutions and technology roadmaps.
Establish development standards, best practices, and governance processes that improve solution quality and consistency.
Serve as a subject matter expert for enterprise data integration, warehousing, and analytics technologies.