Position Overview
The ideal candidate plays a critical role in designing, building, and maintaining data pipelines and infrastructure within an Azure environment. This position is responsible for transforming raw data into actionable insights, ensuring data quality, and optimizing data workflows to support analytics initiatives across the organization.
Note, this is a remote position with the ideal candidate residing near one of our office site in Colorado Springs, CO., Dayton, OH., Alexandria, VA., or Huntsville, AL.
Please Note: U.S. Citizenship is required.
Role and Responsibilities:
Responsibilities and duties may include, but are not limited to:
- Design and implement efficient data models for structured and unstructured data. Optimize data storage strategies, including partitioning and indexing, to improve query performance and reduce costs.
- Ensure pipelines are optimized for performance, scalability, and reliability to handle large-scale datasets with integrated security measures, such as encryption, authentication, and automated vulnerability scanning.
- Work closely with DevSecOps engineers, data scientists, analysts, and business stakeholders to translate business requirements into technical solutions ensuring that data engineering processes align with organizational security policies.
- Lead the migration of on-premises data to Microsoft Azure cloud environments, maintaining clear documentation of data pipelines, and governance processes for sustainability and knowledge transfer.
- Track the performance of data pipelines and workflows. Identify bottlenecks, troubleshoot issues, and implement optimizations to improve data processing efficiency and reduce downtime.
Required Experience:
- Expertise in Azure services like Azure Synapse, OneLake, and Data Factory for building scalable data pipelines. Knowledge of Azure security practices, including data encryption, access control, and compliance with data governance frameworks.
- Experience with DevSecOps practices, including security automation, vulnerability management, and secure coding standards
- Proficiency in programming languages (e.g., Python, SQL, Java) and familiarity with data modeling tools such as Cameo, EASparx, ETL processes, and cloud platforms (like Azure or AWS).
- Proven experience in integrating data from various sources into unified data models, ensuring that the architecture supports efficient data flow and accessibility with an understanding of medallion architecture (Bronze, Silver, Gold layers) for organizing data in Azure.
- Demonstrated experience with UAF, DoDAF, or other architecture frameworks and related tools and the ability to interpret and work with architecture artifacts.
- Solid understanding of data modeling, data warehousing concepts, and data governance practices with ability to analyze complex data sets and derive actionable insights, demonstrating strong problem-solving abilities.
- Familiarity with Cloud providers (AWS, Google, etc.), Databricks, or other industry data platforms.
Qualifications and Education Requirements:
- Bachelor’s or master’s degree in computer science, Data Science, or a related field.
- Minimum 5 years experience; 8 years experience preferred.
- 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 be able to obtain a Secret Clearance
EEO Statement:
MTSI embraces nine core values including our first core value of Employees come first. Consistent with our Core Values, we are committed to hiring and retaining a diverse workforce. We are proud to be an Equal Opportunity/Affirmative Action-Employer, making decisions without regard to race, color, religion, creed, sex, sexual orientation, gender identity, marital status, national origin, age, military/veteran status, disability, or any other characteristics protected by law. MTSI is committed to Equal Employment Opportunity and providing reasonable accommodations to applicants with physical and/or mental disabilities.
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