MANTECH seeks a motivated, career and customer-oriented Lead Data Engineer to join our Enterprise Data, AI, and Automation team in Herndon, VA. This is a hybrid position, requiring 2-3 days a week onsite.
In this pivotal role, you will bridge the gap between complex raw data and actionable business intelligence, ensuring that our data is standardized, reliable, and ready for reporting, AI/ML engineering, and other downstream applications. You will also guide the transformation of our legacy data warehouse to a modern, data lakehouse platform.
Responsibilities include but are not limited to:
- Enterprise Pipeline Engineering: Design, build, and maintain secure, scalable ETL/ELT pipelines integrating disparate enterprise business systems and API connections into a unified enterprise data model.
- Data Warehousing & Modeling: Develop/update data warehouse schema to align with our evolving business requirements for reporting, analytics, and automation.
- Tooling Strategy: Act as a subject matter expert in the selection and implementation of next-generation analytics platforms and data engineering tools.
- Lakehouse & Semantic Architecture: Drive the migration toward a modern data lakehouse and assist analytics engineers with implementation of a universal semantic data model.
- Data Quality & Observability Infrastructure: Implement automated data validation, lineage tracking, and end-to-end observability to ensure high data fidelity and pipeline reliability across the enterprise platform.
- Proactive Alerting & Executive Intelligence: Support analytics engineers to implement automated orchestration logic and alerting triggers that notify business leaders in real time when key performance metrics cross predefined limits.
- Team Mentorship & Support: Guide teammates on modern data engineering practices and architect data flows optimized for consumption by machine learning models and AI applications.
Minimum Qualifications:
- Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related field with 7+ years of data engineering experience, including at least 2+ years leading engineering initiatives or architectural design for enterprise systems.
- Proven experience integrating common enterprise systems via database connectors or APIs.
- Hands-on experience working with a data lakehouse architecture and proven knowledge of tradeoffs associated with data modeling approaches.
- Mastery in writing complex, optimized SQL queries and managing relational database schemas.
- Experience with modern cloud data platforms, transformation tools, and code-driven orchestration tools (e.g., Apache Airflow, Dagster, Prefect).
- Experience with common software tools (e.g., Azure DevOps, GitHub) for CI/CD and version control of data infrastructure.
Preferred Qualifications:
- Proficiency in Python and PySpark for data engineering, data manipulation, API consumption, and automation scripts.
- Experience using Informatica for API and database extraction.
- Experience implementing automated data quality, monitoring, and lineage tooling (e.g., Monte Carlo, Great Expectations, Soda, or Databricks Unity Catalog).
- Experience building data pipelines for machine learning, natural language processing, or vector database/RAG workflows.
- Experience with Docker and Infrastructure-as-Code (Terraform or CloudFormation).
Clearance Requirements:
Physical Requirements:
- Must be able to remain in a stationary position 50%.
- Needs to occasionally move about inside the office to access file cabinets, office machinery, etc.
- Frequently communicates with co-workers, management, and customers, which may involve delivering presentations. Must be able to exchange accurate information in these situations.