Navy Federal Credit Union currently does not provide sponsorship for this role. Applicants must be authorized to work in the United States without the need for current or future sponsorship.
The Senior Data Engineer is an experienced technical lead responsible for accelerating NFCU’s transition to AI-enabled data engineering by applying modern AI engineering, data engineering, and intelligent automation practices across the enterprise delivery lifecycle. This role will design and implement scalable data engineering patterns, reusable automation frameworks, and autonomous or human-guided AI agents that reduce manual effort, improve engineering productivity, strengthen delivery quality, and advance an AI-enabled data engineering SDLC across planning, architecture, design, development, testing, deployment, monitoring, and production support.
The Senior Data Engineer will lead the practical adoption of Generative AI, Agentic AI, Retrieval-Augmented Generation, Lakehouse and Medallion architectural patterns. The role will automate repeatable engineering activities and establish secure, governed, and reusable AI-first engineering standards. It requires hands-on expertise with Microsoft Foundry, Azure AI services, Databricks Mosaic AI, LangChain, LangGraph, vector search technologies, agent orchestration frameworks, Apache Spark, Python, SQL, Databricks, and Microsoft Fabric to deliver reliable, observable, and production-ready data engineering and AI automation solutions.
- Lead the design, development, and deployment of AI-enabled data engineering accelerators, reusable frameworks, and intelligent agent-based solutions that automate and optimize activities across the data engineering lifecycle.
- Apply Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), vector search technologies, and agent orchestration frameworks to automate data ingestion, transformation, validation, testing, metadata generation, lineage documentation, reconciliation, monitoring, and production support processes.
- Design and implement scalable Lakehouse and Medallion architecture patterns using Databricks, Microsoft Fabric, Apache Spark, Python, SQL, and modern cloud-based data integration and analytics technologies.
- Establish AI-first engineering standards, design patterns, reusable prompts, agent frameworks, and governance practices that enable secure, responsible, and scalable adoption of AI across enterprise data engineering teams.
- Develop and optimize production-ready data pipelines and automation capabilities with a strong focus on reliability, observability, data quality, reconciliation, resiliency, monitoring, alerting, and service-level objective adherence.
- Collaborate with data architects, platform engineering teams, DevSecOps, product owners, governance teams, and business stakeholders to translate strategic business needs and technical requirements into scalable, maintainable, and secure solutions.
- Evaluate emerging AI and data engineering technologies, identify high-value enterprise use cases, and recommend adoption strategies aligned with NFCU architecture, information security, governance, risk management, and operational standards.
- Lead the implementation of Agile delivery practices, CI/CD automation, test engineering, deployment strategies, operational readiness, production support, and continuous improvement for AI-enabled data engineering solutions.
- Mentor engineers, guide implementation decisions, promote engineering excellence, and drive consistent adoption of secure, governed, and measurable AI-enabled delivery improvements.
- Ensure the security, integrity, traceability, and compliance of data systems and automation solutions in alignment with Navy Federal, industry engineering, information security, data governance, and regulatory expectations.
- Bachelor’s degree in Information Systems, Computer Science, Engineering, Data Engineering, or a related field, or the equivalent combination of education, training, and experience.
- Advanced hands-on experience in data engineering using Apache Spark, Python, SQL, Databricks, Microsoft Fabric, and modern cloud-native data and analytics technologies.
- Experience designing and implementing scalable batch, near real-time, and real-time data pipelines within Lakehouse and Medallion architecture environments.
- Experience developing AI-enabled data engineering accelerators, developer productivity tools, intelligent assistants, reusable frameworks, and automation capabilities that improve software delivery and operational efficiency.
- Hands-on experience applying Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), vector search technologies, agent orchestration frameworks, and related AI engineering capabilities.
- Experience with Microsoft Foundry, Azure AI Services, Databricks Mosaic AI, or comparable AI development platforms for building secure, scalable, and production-ready AI-enabled solutions.
- Strong understanding of data quality, testing, validation, reconciliation, metadata management, data governance, lineage, observability, monitoring, alerting, and production support practices.
- Working knowledge of DevSecOps, Azure DevOps, CI/CD automation, Agile delivery methodologies, test engineering, deployment practices, operational readiness, and site reliability principles.
- Ability to design secure, scalable, reusable, and governed engineering patterns that support enterprise data platforms and analytics modernization.
- Excellent analytical, problem-solving, communication, and leadership skills, with the ability to explain complex technical concepts to engineering teams, business stakeholders, and technology leaders.
Desired Qualifications
- Master’s degree in computer science, Information Technology, Engineering, Data Engineering, Analytics, or a related discipline.
- 7 to 10 years of experience delivering advanced data engineering capabilities across cloud platforms, big data ecosystems, Lakehouse architecture, and governed enterprise data platforms.
- Experience modernizing legacy data engineering processes into cloud-native, AI-ready, automated, and governed delivery models.
- Knowledge of financial services data governance, regulatory traceability, audit readiness, data privacy, information security, and operational risk management expectations.
- Demonstrated ability to mentor engineering teams, influence technical standards, drive adoption of emerging technologies, and deliver measurable productivity and operational improvements.
- Knowledge of Navy Federal Credit Union instructions, standards, and procedures.
Additional Information
Hours:
- Monday - Friday, 8:00AM - 4:30PM
Location:
- 820 Follin Lane, Vienna, VA 22180