Will design and build centralized data warehouses and operational data stores by developing ETL pipelines that extract, transform, and load data from multiple systems including Microsoft Azure DevOps, SQL Server, SSAS, SharePoint, Excel, and other enterprise platforms using tools such as SQL Server Integration Services (SSIS), Python (pandas, pyodbc), Alteryx, and Azure Data Factory. Utilize advanced data analytics and machine learning algorithms such as Natural Language Processing (NLP), Random Forest, Decision Trees, Logistic Regression, and Gradient Boosting to derive insights from structured and unstructured data. Use Python libraries including scikit-learn, NLTK, pandas, NumPy, XGBoost, and TensorFlow to develop, validate, and deploy predictive models. Integrate models via Azure Machine Learning Service and RESTful APIs to support automated decision systems and risk monitoring frameworks. Create and manage data pipelines and reusable data libraries using Python and Power BI Dataflows to ensure continuous, automated ingestion, transformation, and enrichment of data from structured and semi-structured sources. Develop interactive and scalable dashboards and reports using data visualization tools such as Power BI and Tableau. Create DAX and M queries, implement row-level security (RLS), set up data refresh schedules, and publish content to Power BI Service to support self-service analytics and operational reporting. Analyze existing data processing workflows and enterprise software applications to identify performance bottlenecks, data quality issues, and design gaps; propose and implement process improvements, ETL optimizations, and automation strategies to enhance data flow, integrity, and accessibility. Develop and maintain centralized data repositories and data marts, collaborate with data architects and business stakeholders to define schemas, relationships, and indexing strategies aligned with business reporting needs. Perform data wrangling, transformation, and enrichment using SQL and Python across sources such as SQL Server, SharePoint, Clarity, Azure Data Lake, REST APIs, and flat files. Write scripts to authenticate and call external APIs (using OAuth2, requests, etc.), parse JSON\/XML payloads, flatten nested data structures, apply business rules, and load into target schemas. Use T-SQL, window functions, and CTEs to perform aggregations, joins, and logic-based transformations. Enable automation of business processes by developing Robotic Process Automation (RPA) solutions using Microsoft Power Automate and Katalon Studio. Design scripts and workflows that automate data ingestion, notifications, file processing, and user interactions, reducing manual workload and increasing operational efficiency. Translate business requirements into functional specifications and technical documentation for dashboards, data models, and integrations. Write advanced SQL, Hive, and Impala queries to retrieve and manipulate large-scale datasets from various database systems for business reporting, analytical modeling, and KPI tracking. Analyze internal and external business requirements for software and reporting enhancements, evaluating impact of new APIs or data sources, and developing test plans to ensure successful integration and data validation. Develop and maintain REST API integrations to support system interoperability, process automation, and real time data exchange. Writing scripts in Python or PowerShell to handle authentication, data transfer, notifications, and API-based logging. \u00a0Position requires up to 100% domestic travel. This position is for full-time, salaried (W-2), permanent employment.<\/p>\n
Position requires a Master’s Degree or foreign equivalent in Information Technology, Computer Science, Data Science or a related field plus 1 year of experience, pre or post Master\u2019s Degree, in Data Analysis.<\/p>\n