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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Azure Data Architect - **Company:** Sparibis Llc - **Location:** Washington, DC, United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Microsoft Azure, Big Data, CompTIA Security+, Databases, Data Validation, Data Cleansing, Data Governance, Extract Transform Load (ETL), Data Mining, Data Visualization, R (Programming Language), Python (Programming Language), Machine Learning, Meta-Data Management, Power BI, SQL Databases, Data Storage Technologies, Cloud Platform System, Microsoft Power Automate, Azure Data Factory, Data Lakes, Pyspark, Information Technology, Machine Learning Operations, Azure Synapse Analytics, Databricks - **Published:** August 12, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=e9a09c0bd8cbcc13 ## About the Role Years' Experience: 10+ Years of professional experience. Education: Bachelor's degree in computer science, Data Science, Statistics, or related field. Clearance: Applicants must be able to obtain and maintain a secret security clearance. United States Citizenship is required as part of the eligibility criteria to be able to obtain this type of security clearance. Certifications: * Active CompTIA Security+ Certification Key Skills: * Expertise in statistical modeling, machine learning algorithms, and data mining techniques. * Must have strong expertise in the Azure Cloud environment * Strong proficiency in Python or similar language and SQL for data preparation, analysis, and modeling, * 10+ years of experience in data science, machine learning, and statistical modeling. * Bachelor's degree in data science, Statistics, Mathematics, Computer Science, or a related quantitative field experience * Active Secret security clearance * Active CompTIA Security+ certification * Expertise in statistical modeling, machine learning algorithms, and data mining techniques. * Experience with cloud computing platforms (e.g., Azure, Data Lake, Synapse Analytics, Machine Learning, Databricks, Logic Apps, AI Foundry). * Proficiency in programming languages such as Python, R, SQL, PySpark * Strong proficiency in Python or similar language and SQL for data preparation, analysis, and modeling * Experience with data visualization tools (e.g., Power BI). * Hands on experience with Microsoft Dataverse for data storage and management. * Strong experience with data extraction, cleaning, and transformation (ETL) processes. * Experience in data governance, metadata management, and data cataloging. * Experience with cloud computing platforms (e.g., Azure, Data Lake, Synapse Analytics, Machine Learning, Databricks, Logic Apps, AI Foundry). * Strong understanding of database technologies and SQL. * Experience with Microsoft Power Platform (Power BI, Power Automate) is a plus. * Excellent communication, presentation, and leadership skills. ## Description * Data Science Strategy: Develop and implement a data science strategy for the project, aligning with strategic goals and objectives. * Problem Identification: Work with stakeholders to identify opportunities to leverage data to improve HR processes, such as recruitment, retention, training, and performance management. * Data Exploration & Analysis: Lead the exploration and analysis of large datasets to identify trends, patterns, and insights to understand data quality, gaps, and potential enhancements. * Model Development: Design, develop, and validate predictive models using machine learning algorithms and statistical techniques. * Model Deployment: Deploy and maintain machine learning models in a production environment, ensuring scalability and reliability. * Data Visualization: Create compelling data visualizations using tools such as Power BI to communicate insights to stakeholders. * Team Leadership: Lead and mentor a team of data scientists, providing technical guidance and support. * Collaboration: Collaborate with other teams, including software developers, system architects, and business analysts, and to integrate data science solutions. * Data Quality: Ensure data quality and accuracy by implementing data validation and cleansing processes. * Compliance: Ensure that all data science activities comply with relevant and DoD regulations and security policies. * Research & Development: Stay up to date with the latest advances in data science and machine learning and explore new technologies and techniques to improve HR processes. * Documentation: Create and maintain detailed documentation of data science processes, models, and results. ## Related Videos - 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