Senior Data Engineer

The Enterprise Data Engineer will play a pivotal role in enabling data-driven decision-making by designing, implementing, and maintaining scalable data infrastructure and systems across the enterprise environment. This position requires expertise in data architecture, data organization, data pipelines, analytics platforms, and cloud-based solutions to ensure efficient and secure data access for stakeholders. The ideal candidate will work closely with cross-functional teams and collaborate with business units to optimize data processing, ensure data integrity, and support enterprise-wide digital transformation initiatives. This principle near term emphasis will be supporting a funded 2026 IRAD focused on large data organization and quality processes to facilitate downstream AI tool implementation. Additionally, the position supports solutioning data architectures for current and future AMD customer base to support digital transformation, proposal solutioning for data architecture tasks, as well as design and implementation of data architectures as part of direct customer support.

Responsibilities and duties may include, but are not limited to:

• Support IRAD Project Lead to develop and implement data meta-tags to meet IRAD technical goals
• Working closely with the AMD Digital ecosystem lead, design, develop, and maintain scalable data systems, including data warehouses, data lakes, and big data platforms to enable customer digital transformation requirements.
• Optimize data storage solutions by building robust architectures that meet modern enterprise requirements.
• Support digital ecosystem and data solutioning to support digital transformation capture activities
• Provide technical approaches for data -centric solutioning as part of the proposal effort.
• 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.
• 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.
• Interface with MTSI customers on data -centric efforts to define and implement customer requirements
• Collaborate with business teams, analytics managers, data scientists, and IT teams to align enterprise data solutions with organizational goals.
• Occasional travel to MTSI offices and events throughout country

Qualifications:

• Bachelor's or master's degree in computer science, Data Science, or a related field.
• 5+ years of relevant experience
• Active Secret Clearance
• Demonstrated ability to communicate complex data issues to both technical and non-technical stakeholders.
• Hands on experience with proven results delivering executable solutions for "Big Data" commercial or DoD challenges
• 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.
• 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.

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