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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Azure Data Architect - **Company:** Sparibis Llc - **Location:** Orlando, FL, 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, Data Lakes, Pyspark, Information Technology, Machine Learning Operations, Azure Synapse Analytics, Databricks - **Published:** August 22, 2026 - **Apply:** https://www.dice.com/job-detail/442a6f08-70cf-4306-a31a-e70f24f555dd ## 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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