Hybrid Data Engineer

SMX

Data Engineer building scalable data architecture and data pipelines for AI and analytics at SMX. Collaborating with Analysts and AI Engineers in optimizing data systems and ensuring reliability.

Posted 7/6/2026full-timeHanover • Maryland • 🇺🇸 United StatesMid-LevelSenior💰 $103,000 - $171,800 per yearWebsite

Tech Stack

Tools & technologies

AirflowAmazon RedshiftApacheAWSAzureCloudETLGoogle Cloud PlatformJavaKafkaPythonSparkSQL

About the role

Key responsibilities & impact

Design, construct, install, test, and maintain highly scalable data management systems and robust ELT/ETL pipelines across cloud and on-prem systems.
Build infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using cloud technologies. Support API strategy, data modeling, and domain-driven data structures.
Implement monitoring systems to ensure data integrity, quality, and security across all 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, design, and implement internal process improvements, such as automating manual processes and optimizing data delivery for greater scalability. Automate ingestion and transformation processes using industry-standard tools and patterns. Troubleshoot pipeline issues and optimize for performance and cost.
Work with analytics and business teams to understand their data requirements and deliver production-ready datasets. Collaborate with AI engineers and analysts to ensure datasets meet requirements.

Requirements

What you’ll need

Bachelor's degree in Computer Science, Information Technology, Data Engineering
4+ years of progressive professional experience in a data engineering or backend software engineering role
Security clearance required (Secret or higher)
Strong proficiency in Python, SQL, Java.
Experience with cloud data warehouses such as Snowflake and AWS Redshift.
Familiarity with distributed computing tools like Spark, Databricks, and Kafka.
Experience with Azure Data Factory, AWS Glue, or similar data integration platforms.
Experience with workflow management tools like 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.

Benefits

Comp & perks

Health insurance
Paid leave
Retirement

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

Data EngineeringBackend Software EngineeringData ModelingSchema DesignData Quality ChecksMetadata ManagementAutomation of Data ProcessesData IntegrationCloud Data WarehousingData Pipeline Optimization

Certifications

Bachelor's Degree in Computer ScienceSecurity Clearance (Secret or Higher)