About this opportunity
Jobtailor lists this Principal Data Engineer opportunity in vienna, Virginia. Review the employer’s description below for duties, qualifications and application requirements.
Job description
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 API‑driven and event‑based data onboarding patterns using Gravitee, MuleSoft, Kafka, and related technologies
Establish reusable engineering ETL frameworks, design patterns, and automation 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 Unity Catalog, Alation, and related governance capabilities
Partner 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 engineering, information security, data governance, and regulatory expectations
Support Agile delivery, DevSecOps, CI/CD deployment, release readiness, defect resolution, and production support
Mentor senior and mid‑level engineers, promote engineering excellence, and drive adoption of enterprise standards
Requirements
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 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
Applicants must be authorized to work in the United States without the need for current or future sponsorship
Ability to work Monday–Friday, 8:00AM–4:30PM
Core Competencies
Demonstrates advanced expertise in designing and optimizing scalable data pipelines and cloud‑native analytics solutions using Spark, Python, SQL, and Azure services. Proven ability to lead technical teams, implement data governance practices, and ensure compliance in complex enterprise environments.
Highest‑signal resume keywords
Spark
Python
SQL
Azure Data Factory
Data Governance
ATS Optimization Keywords
Hard Skills
Data Engineering
Data Pipeline Development
Automated Data Quality Validation
Metadata Management
Data Reconciliation
Lakehouse Architecture
Data Warehousing
Analytical Data Stores
ETL Frameworks
API‑Driven Data Onboarding
Soft Skills
Problem‑Solving
Communication
Leadership
Mentoring
Architectural Judgment
Industry Keywords
Agile Delivery
DevSecOps
CI/CD
Data Governance
Regulatory Compliance
Tools & Technologies
Databricks
Microsoft Fabric
Gravitee
MuleSoft
Kafka
Unity Catalog
Alation
Power BI
Tableau
Azure DevOps
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Worksite address
vienna, VA, 22184, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.