Data Engineer - Journeyman

Modern Technology Solutions Inc•Dayton, OH

2d•$105,000 - $125,000•Remote

About The Position

Note: This is a remote position with the ideal candidate residing near one of our office sites in Colorado Springs, CO., Dayton, OH., or Huntsville, AL. Required Experience: Data Warehousing & Analytics (Core Requirement): Advanced proficiency in SQL with extensive experience in cloud data warehousing. Must have deep knowledge of data modeling, schema design, and query optimization. ○ GCP Preference: Significant, hands-on experience with Google BigQuery (partitioning, clustering, BQ scripting, cost optimization) is essential. Modern Data Pipeline Expertise (Must-Have Foundation): Demonstrable expertise in building, deploying, and scaling complex data pipelines using at least one of the following foundational technologies: ○ Orchestration: Deep expertise in Apache Airflow (designing, deploying, scaling, and managing complex DAGs). GCP Preference: Hands-on experience with Google Cloud Composer. ○ Processing: Proven ability to build and optimize robust, high-throughput batch and streaming data pipelines using Apache Beam. GCP Preference: Direct experience managing Beam pipelines at scale using Google Cloud Dataflow. ○ (Note: This role offers the flexibility to architect solutions and select the appropriate services to meet project requirements). Relational Database Expertise (Must-Have Foundation): Solid foundation and practical experience with RDBMS architecture, management, and optimization, specifically with PostgreSQL. ○ GCP Preference: Familiarity with managed database services, particularly Google Cloud SQL (for PostgreSQL or MySQL), is a significant advantage. Core Programming & Infrastructure: Fluency in Python (preferred) or Java for data pipeline development. Strong understanding of CI/CD, Git, and Infrastructure-as-Code (e.g., Terraform). Cloud Architecture & GCP (Highly Desirable): Broad experience in architecting, building, and managing solutions on a major cloud platform, with a strong preference for Google Cloud Platform (GCP) beyond just its data services. Containerization & Kubernetes (Highly Desirable): Understanding of container concepts (Docker) and practical experience with Kubernetes, particularly Google Kubernetes Engine (GKE). ML Engineering Exposure (Plus): Experience supporting machine learning workflows, including data preparation, feature engineering, and operationalizing data pipelines for ML models. ○ GCP Preference: Any exposure to Google Cloud Vertex AI (Pipelines, Feature Store, Training) is a major plus. Qualifications and Education Requirements: Bachelor’s or master’s degree in computer science, Data Science, or a related field. 5-8 years of experience of hands-on experience in data engineering, demonstrating a clear progression in designing, building, and maintaining scalable data-intensive systems. Demonstrated ability to communicate complex data issues to both technical and non-technical stakeholders. Travel: Occasional travel to MTSI offices and events throughout country Clearance: Must have a Secret Clearance; US Citizenship required. The pay range for this position in Colorado is $105,000/year to $125,000/year; however, base pay offered may vary depending on established government contract rates, job-related knowledge, skills, and experience, and other factors. MTSI also offers a full range of medical, financial, and other benefits, dependent on the position offered. This information is provided per the Colorado Equal Pay Act. Base pay information is based on market location. Applicants should apply via MTSI’s internal or external careers site #LI-MS1 #HYBRID #MTSI

Requirements

Advanced proficiency in SQL with extensive experience in cloud data warehousing.
Deep knowledge of data modeling, schema design, and query optimization.
Significant, hands-on experience with Google BigQuery (partitioning, clustering, BQ scripting, cost optimization) is essential.
Demonstrable expertise in building, deploying, and scaling complex data pipelines using at least one of the following foundational technologies
Deep expertise in Apache Airflow (designing, deploying, scaling, and managing complex DAGs).
Proven ability to build and optimize robust, high-throughput batch and streaming data pipelines using Apache Beam.
Solid foundation and practical experience with RDBMS architecture, management, and optimization, specifically with PostgreSQL.
Fluency in Python (preferred) or Java for data pipeline development.
Strong understanding of CI/CD, Git, and Infrastructure-as-Code (e.g., Terraform).
5-8 years of experience of hands-on experience in data engineering, demonstrating a clear progression in designing, building, and maintaining scalable data-intensive systems.
Demonstrated ability to communicate complex data issues to both technical and non-technical stakeholders.
Must have a Secret Clearance
US Citizenship required.

Nice To Haves

Hands-on experience with Google Cloud Composer.
Direct experience managing Beam pipelines at scale using Google Cloud Dataflow.
Familiarity with managed database services, particularly Google Cloud SQL (for PostgreSQL or MySQL), is a significant advantage.
Broad experience in architecting, building, and managing solutions on a major cloud platform, with a strong preference for Google Cloud Platform (GCP) beyond just its data services.
Understanding of container concepts (Docker) and practical experience with Kubernetes, particularly Google Kubernetes Engine (GKE).
Experience supporting machine learning workflows, including data preparation, feature engineering, and operationalizing data pipelines for ML models.
Any exposure to Google Cloud Vertex AI (Pipelines, Feature Store, Training) is a major plus.