Lead Data Engineer

Archer; Herndon (VA); Hybrid; USD 140,000 - 170,000; Full time

Job summary

MANTECH seeks a motivated, career- and customer-oriented Lead Data Engineer to join our Enterprise Data, AI, and Automation team in Herndon, VA. This hybrid role requires 2-3 days onsite weekly and offers the opportunity to bridge complex raw data into actionable BI, AI/ML pipelines, and modern lakehouse architectures.

You will design and maintain ETL/ELT pipelines, develop data warehouse schemas, guide tooling strategy, migrate to a data lakehouse, and build observability, data quality, and

Qualifications

• 7+ years of data engineering experience with 2+ years in leadership or architecture.
• Experience integrating enterprise systems via connectors or APIs.
• Hands-on data lakehouse architecture and data modeling tradeoffs.
• Strong SQL and relational schema management.
• Experience with cloud data platforms, orchestration tools, CI/CD for data infra.

Responsibilities

• Design, build, and maintain secure ETL/ELT pipelines across systems.
• Develop/update data warehouse schemas for reporting and analytics.
• Guide tooling strategy for analytics platforms and data engineering tools.
• Drive migration to a modern data lakehouse and universal semantic model.
• Implement data quality, lineage, observability, and real-time alerting.
• Mentor teammates on modern data engineering practices.

Skills

Data engineering

SQL proficiency

Cloud data platforms

ETL/ELT pipelines

Data modeling

Mentorship

Education

Bachelor's degree in Computer Science / Information Systems / Data Engineering

Tools

Python

PySpark

Apache Airflow

Dagster

Prefect

Informatica

Docker

Terraform

CloudFormation

Azure DevOps

GitHub

Job description

Archer is collaborating with MANTECH to connect them with exceptional professionals for this role.

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

• U.S. Citizen

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.