Senior Data Engineer

Location: Remote (DC)

Duration: 1+ Year

Federal Requirement

Mandatory Education and Experience

Consultants must meet one of the following education or overall experience requirements:

• Bachelor's degree in data engineering, computer science, data science, machine learning, mathematics, information systems, or a related discipline; or
• At least five years of applied professional experience in one or more of these fields.

Five or More Years of Hands-On Experience in Each Area

• Maintaining SQL databases.
• Conducting advanced SQL and T-SQL operations.
• Designing, implementing, and maintaining ELT or ETL processes.
• Supporting ELT or ETL processes within cloud-based data-analytics environments.

Three or More Years of Hands-On Experience in Each Area

• Working with Azure Synapse Analytics.
• Working with Azure Machine Learning and the modern Microsoft data stack.
• Manipulating, cleansing, transforming, and processing data using Python.
• Using the Python Pandas library.

Preferred Qualifications

• Microsoft Certified: Azure Data Engineer Associate, DP-203, or equivalent certification.
• Experience using PySpark or Polars.
• Experience developing reusable, modular, and testable Python code.
• Experience implementing pipelines and cloud infrastructure through code-first approaches.
• Experience with Python SDKs, Azure CLI, REST APIs, infrastructure-as-code tools, or comparable automation methods.
• Experience implementing Git-based source control.
• Experience creating continuous-integration and continuous-delivery workflows.
• Experience with Azure Data Lake Storage.
• Experience with Azure Virtual Machines and Azure Monitor.
• Experience with Parquet and other modern analytical-data formats.
• Familiarity with AI coding assistants and LLM-integration patterns.
• Experience supporting machine-learning, NLP, fraud analytics, or investigative-data environments.
• Experience documenting enterprise data architecture and developing technical SOPs.

Technical Skills

• Microsoft Azure
• Azure Synapse Analytics
• Azure Machine Learning
• Azure Machine Learning SDK V1 and V2
• Azure Data Lake Storage
• Azure Monitor
• SQL and T-SQL
• SQL Server
• Python and Pandas
• PySpark or Polars
• ELT and ETL pipeline development
• Git and source control
• CI/CD
• REST APIs
• Infrastructure as Code
• Parquet
• Data modeling
• Data normalization
• Error handling and logging
• Pipeline monitoring
• Data dictionaries and ER diagrams
• Cloud-cost optimization