Description

• Design, build, and maintain scalable data pipelines across on-premises and cloud platforms (Azure, Databricks, Microsoft Fabric, GCP, AWS) to ingest, transform, and store diverse datasets in support of enterprise business use cases. 
• Develop, optimize, and maintain data models, including dimensional models (star and snowflake schemas), to improve query performance, scalability, and usability for analytics and reporting. 
• Integrate data from a variety of sources, including relational databases, NoSQL platforms, APIs, and files, applying AI-enabled data integration techniques such as intelligent data mapping, schema discovery, metadata enrichment, and automated data quality validation to improve accuracy and efficiency. 
• Enhance ETL/ELT processes through optimization, automation, and performance tuning to improve scalability, reduce bottlenecks, and support high-volume data processing.  
• Develop and operate end-to-end ETL/ELT workflows using tools such as SSIS, Azure/Fabric Data Factory, Dataflows, and Notebooks, incorporating data validation, error handling, logging, monitoring, and scheduling to ensure reliable data operations.  
• Automate data pipeline deployment and operations through CI/CD practices, including automated testing, release management, and monitoring to enable faster and more reliable delivery. 
• Support the management and governance of enterprise data platforms, including data lakes, data warehouses, security controls, and access management. 
• Partner with architects, developers, and stakeholders to translate requirements into solutions, and prepare curated data marts and fact/dimension tables to support analytics. 

Data Analytics: 

• Analyze datasets to identify trends, patterns, and anomalies. Use statistical methods, DAX, Python, and R to generate insights that inform business strategies. 
• Develop interactive Power BI dashboards and reports, leveraging DAX to create calculated columns and measures, monitor key performance indicators, deliver service dashboards, and communicate results effectively to stakeholders. 
• Build predictive or descriptive models using statistical, Python, or R-based machine learning methods. Design and integrate data models to improve service delivery. 
• Present findings to non-technical audiences in clear, actionable terms. Translate complex data into business-focused insights and recommendations. 
• Deliver analytics solutions iteratively in an Agile environment. Mentor teams to enhance analytics fluency and support self-service capabilities. 
• Provide data-driven analysis, visualizations, and AI-enabled insights to support corporate priorities, strategic initiatives, and informed decision-making. 

Key Skills

Education

Bachelor's degree