Data Engineer – Onsite, MD

Data Engineer(s):

·       Understand and prioritize business problems and identify ways to leverage data to recommend solutions to business problems. Organize and synthesize data into actionable business decisions, focused on insights.

·         Provide insight into trends, financial and business operations through data analysis and the development of business intelligence visuals.

·         Analyze complex datasets related to NIH facility operations (e.g., climate control systems, aseptic facilities) to identify trends, patterns, and anomalies.

·    Develop and apply statistical models and machine learning algorithms to extract actionable insights from data, focusing on areas like energy efficiency, operational improvements, and predictive maintenance.

·        Translate data analysis results into clear, concise reports and presentations for stakeholders at all levels.

·        Use data visualization tools like Power BI, Tableau, and Azure Grafana to create dynamic dashboards and reports that effectively communicate data-driven insights.

·         Expert in Spark SQL and Spark Data Frames using Scala for Distributed Data Processing.

·         Develop Data Frame and RDD (Resilient Distributed Datasets) to achieve unified transformations on the data load.

·     Design, build, and maintain robust data pipelines using Azure Event Hubs, Data Lake Storage, and Databricks to ingest, process, and store data from various sources, including real-time data from climate control systems.

·         Implement data quality checks and cleansing processes to ensure data accuracy and consistency.

·         Utilize Azure DevOps and Infrastructure as Code(IaC) principles to manage and automate data pipeline deployments and updates.

·    Develop and deploy machine learning models using Python and Azure Machine Learning to predict equipment failures, optimize energy consumption, and improve operational efficiency.

·       Explore and implement advanced machine learning techniques, including deep learning, ensemble methods, and natural language processing (NLP), to enhance prediction accuracy and address complex analytical challenges.

·         Utilize Azure Synapse Analytics for data exploration, integration, and analysis to support model development and deployment.

·         Manage the source code in GitHub.

·         Track and delivery requirements in Jira.

·      Model, design, develop, code, test, debug, document and deploy application to production through standard processes also in addition build business models using Data science skills.

·       Harmonize, transform, and move data from a raw format to consumable and curated view.

·     Apply strong Data Governance principles, standards, and frameworks to promote data consistency and quality while effectively managing and protecting the integrity of corporate data.

·     Contribute to the development and implementation of data security strategies, including permissions management, data classification, and threat detection, using Azure security tools and services.

·       Collaborate effectively with other data scientists, subject matter experts, and stakeholders to address data quality issues and achieve project objectives.

·       Share knowledge and insights on data science best practices and emerging technologies through workshops and presentations.

·      Actively participate in team meetings and discussions using Azure DevOps and Communication Services to foster a collaborative work environment.