Overview
On Site
USD 105,000.00 - 125,000.00 per year
Full Time
Skills
Analytics
Data Warehouse
Data Modeling
Query Optimization
Clustering
Scripting
Orchestration
Apache Airflow
Streaming
Apache Beam
Data Flow
RDBMS
Optimization
Database
SQL
PostgreSQL
MySQL
Fluency
Python
Java
Extract
Transform
Load
Continuous Integration
Continuous Delivery
Git
Terraform
Cloud Architecture
Management
Cloud Computing
Docker
Kubernetes
Workflow
Machine Learning (ML)
Google Cloud
Google Cloud Platform
Vertex
Artificial Intelligence
Training
Computer Science
Data Science
Data Engineering
Security Clearance
Government Contracts
Finance
Job Details
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.
Google Cloud Platform 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).
Google Cloud Platform 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.
Google Cloud Platform 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.
Google Cloud Platform 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 & Google Cloud Platform (Highly Desirable): Broad experience in architecting, building, and managing solutions on a major cloud platform, with a strong preference for Google Cloud Platform (Google Cloud Platform) 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.
Google Cloud Platform Preference: Any exposure to Google Cloud Vertex AI (Pipelines, Feature Store, Training) is a major plus.
Qualifications and Education Requirements:
Travel:
Occasional travel to MTSI offices and events throughout country
Clearance:
Must have a Secret Clearance; ship 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
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.
Google Cloud Platform 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).
Google Cloud Platform 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.
Google Cloud Platform 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.
Google Cloud Platform 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 & Google Cloud Platform (Highly Desirable): Broad experience in architecting, building, and managing solutions on a major cloud platform, with a strong preference for Google Cloud Platform (Google Cloud Platform) 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.
Google Cloud Platform 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; ship 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
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.