Cleared Hybrid Data Engineer (5781)

SMX•Hanover, MD

1d•$103,000 - $171,800•Hybrid

About The Position

SMX is hiring a Data Engineer to design, build, and maintain scalable data architecture supporting AI, analytics, and reporting. The role collaborates with analysts, AI engineers, and software engineers to transform raw and unstructured data into reliable, accessible data pipelines. It involves optimizing data systems, building them from the ground up, and ensuring data reliability and performance across the enterprise. The position designs and maintains scalable data management systems and ELT/ETL pipelines across cloud and on-premises systems. It builds data extraction, transformation, and loading infrastructure using cloud technologies; supports API strategy, data modeling, and domain-driven data structures; and implements monitoring, quality, security, lineage, and metadata practices. The Data Engineer prepares data for AI/ML models, semantic search, analytics, and mission applications, automates and improves ingestion and transformation processes, troubleshoots pipeline issues, and works with analytics and business teams to deliver production-ready datasets. The position requires a Secret-or-higher security clearance. The proposed salary range is $103,000–$171,800 USD. SMX describes its benefits as including health insurance, paid leave, and retirement, as well as learning and development opportunities. Selected applicants may be subject to a background investigation and/or education verification. SMX does not sponsor employment authorization or immigration-related support for this position.

Requirements

Bachelor's degree in Computer Science, Information Technology, or Data Engineering.
At least 4 years of progressive professional experience in a data engineering or backend software engineering role.
Secret security clearance or higher.
Strong proficiency in Python, SQL, and Java.
Experience with cloud data warehouses such as Snowflake and AWS Redshift.
Familiarity with distributed computing tools such as Spark, Databricks, and Kafka.
Experience with Azure Data Factory, AWS Glue, or similar data integration platforms.
Experience with workflow management tools such as Apache Airflow.
Hands-on experience with AWS, GCP, or Azure cloud environments.
Familiarity with multi-cloud data ecosystem patterns.
Experience integrating structured and unstructured data sources.
Knowledge of schema design, data modeling, and cloud-based storage patterns.

Nice To Haves

Experience supporting AI/ML pipelines, vectorization, or embedding generation.
Experience with data governance, access controls, or sensitive data protection.
Exposure to DevOps/CI-CD patterns for data workflows.
MuleSoft implementation experience.
Experience with real-time data streaming and event-driven architectures.

Responsibilities

Design, construct, install, test, and maintain scalable data management systems and ELT/ETL pipelines across cloud and on-premises systems.
Build infrastructure for extracting, transforming, and loading data from a wide variety of sources using cloud technologies.
Support API strategy, data modeling, and domain-driven data structures.
Implement monitoring systems for data integrity, quality, and security across storage and processing layers.
Implement data quality checks, validation, lineage tracking, and metadata management.
Prepare and optimize data for AI/ML models, semantic search, analytics, and mission applications.
Identify and implement internal process improvements, including automation and scalable data delivery.
Automate ingestion and transformation processes using industry-standard tools and patterns.
Troubleshoot pipeline issues and optimize performance and cost.
Work with analytics and business teams to understand requirements and deliver production-ready datasets.
Collaborate with AI engineers and analysts to ensure datasets meet requirements.