layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

LAYIQ - Principal Data Engineer at Navy Federal Credit Union

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 expertise in Spark, Python, SQL, Databricks, Azure Data Factory, Microsoft Fabric, and cloud-native data integration, transformation, and analytics solutions.
Strong experience in designing, building, and supporting scalable data pipelines, lakehouse architecture, data warehouses, data marts, and analytical data stores.
Expertise in automated data quality validation, data reconciliation, metadata management, lineage, monitoring, alerting, error handling, and SLA management.
Experience with governance and catalog platforms such as Unity Catalog, Alation, or similar tools.
Experience with BI and analytics platforms such as Power BI, Tableau, Microsoft Fabric, and enterprise reporting modernization patterns.
Working knowledge of Azure DevOps, CI/CD pipelines, Agile delivery, production deployment, and operational support practices.
Ability to communicate complex technical concepts clearly to business stakeholders, technology leaders, engineers, and cross-functional delivery teams.
Strong problem-solving skills, architectural judgment, ownership mindset, and ability to lead delivery in complex, highly regulated enterprise environments.
Desired Qualifications
Master’s degree in computer science, Information Technology, Engineering, Analytics, or a related discipline.
Experience modernizing legacy BI environments, Analytical Data Stores, and on-premises reporting platforms into Azure, Databricks, Microsoft Fabric, or similar cloud-native solutions.
Experience supporting enterprise Analytics Data Modernization across business, operational, risk, finance, member, or enterprise data and analytics domains.
Experience with AI-ready data product design, feature preparation, data observability, and governed self-service analytics capabilities.
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, establish reusable frameworks, influence technical standards, and drive enterprise-scale modernization outcomes.
Additional Information
Hours: Monday - Friday, 8:00AM - 4:30PM
Location: 820 Follin Lane, Vienna, VA 22180

Provide technical leadership and hands-on engineering expertise for Enterprise Data and Analytics Modernization initiatives across data engineering, analytics, reporting, and data consumption.
Design, develop, and optimize scalable batch, near real-time, and real-time data pipelines using Spark, Python, SQL, Databricks, Microsoft Fabric, Azure Data Factory, and related Azure cloud services.
Modernize legacy Analytical Data Store and BI workloads by migrating fragmented reporting and data assets into secure, standardized, governed, and cloud-native analytics platforms.
Lead the design and implementation of API-driven and event-based data onboarding patterns leveraging enterprise API management, integration, and streaming platforms (Gravitee, MuleSoft, Kafka, and related technologies) to deliver secure, scalable, and reusable data ingestion capabilities for analytics, AI, and data products.
Establish reusable engineering ETL frameworks, design patterns, and automation capabilities for ingestion, transformation, validation, reconciliation, monitoring, and production support.
Implement automated data quality validation, reconciliation controls, exception handling, alerting, monitoring, SLA management, and production readiness practices.
Enable metadata management, lineage, cataloging, and access governance through tools such as Unity Catalog, Alation, and related governance capabilities.
Partners with data architects, analysts, product owners, business stakeholders, governance teams, and platform teams to translate business needs into scalable technical solutions.
Guide engineering teams through architecture reviews, implementation decisions, coding practices, design standards, performance tuning, and operational resilience improvements.
Ensure compliance with Navy Federal, industry engineering, information security, data governance, and regulatory expectations for secure and reliable data solutions.
Support Agile delivery, DevSecOps practices, CI/CD deployment, release readiness, defect resolution, and production support for critical modernization deliverables.
Mentor senior and mid-level engineers, promote engineering excellence, and drive adoption of enterprise standards for cloud data engineering and analytics modernization.

Principal Data Engineer

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