Solution Engineer Sr (Azure + AI)

Job Summary:

Responsible for analyzing and gathering business and technical requirements to design and document fit-for-purpose solutions. Contributes to solution development, design, integration and testing, and configuration of technology solutions ensuring that delivered solutions adhere to defined architecture and security standards. Works closely with with cross-functional teams, vendors, and business partners to ensure smooth execution and continuous improvement in solution performance and quality. Ensure seamless solution implementation and effective issue resolution.

Key Responsibilities:

Supports the design of fit-for-purpose solutions by collaborating with stakeholders to identify integration points and ensure alignment with business processes and goals. Supports technical analysis, design and customization of potential solutions including evaluating the technical fit and viability of commercial-off-the-shelf products versus custom built solution. Develops and maintains technical design documents, workflows, and architecture diagrams. Assists in defining and executing the test strategy and validating solutions against business objectives. Ensures smooth interoperability by validating integrations and documenting configuration steps. Identifies and communicates potential technical dependencies and risks. Works with vendors and IT service providers to address technical issues, evaluate and improve product functionality and ensure proper configuration according to design specifications. Designs and implements data migration solutions to ensure data accuracy and reliability. Develops, designs, tests, maintains and configures solutions and systems as needed. Serves as subject matter expert and point of contact on content, processes, procedures and functionality associated with assigned applications for projects and application support (as needed). Documents recommendations to enhance business process efficiency and increase adoption of standard application features and capabilities, aligning solutions with enterprise standards. Supports continuous improvement initiatives by following established design standards and learning to identify potential enhancements in solution design or workflow efficiency. Assists technical and infrastructure groups in understanding the solution design and specifications. Works closely with IT technical service providers to ensure outsourced work packages are delivered to specifications, meeting key parameters of quality, schedule, cost, data privacy, security, performance, and scalability. Participates in formal design reviews, cybersecurity reviews and code reviews (where applicable) to ensure the detailed design specifications have been understood and coding standards and cybersecurity regulations have been adhered to by the development team(s).

Key Responsibilities:

• Data Quality Engineering Automation
  • Design and develop data quality profiling pipelines using PySpark, Python, and Databricks to ensure data accuracy and reliability.
  • Build automated validation, cleansing, and anomaly detection frameworks leveraging ML/AI models.
  • Integrate OpenAI APIs for intelligent rule generation, data summarization, and NLP-based insights.
• Full-Stack Development (minimum 4 years in Web API)
  • Develop responsive React-based user interfaces for self-service data quality monitoring and visualization.
  • Build and maintain RESTAPIs using Node. js and Python (Flask/Fast API) to serve ML models and DQ metrics.
  • Host and manage applications using Azure Web Apps, ensuring secure API connectivity and scalable architecture.
• Cloud and Data Integration
  • Work with Azure SQL Database, Azure Storage, and Databricks for seamless data access and validation.
  • Integrate data pipelines with APIs, Dataverse, Snowflake, or other cloud data sources.
  • Support Azure Application Insights and monitoring integration for performance tracking.
• ML/AI Enablement
  • Contribute to ML projects focused on data quality improvement and predictive monitoring.
  • Apply techniques like anomaly detection, clustering, or deep learning to identify data issues proactively.
  • Use OpenAI / LLM APIs to automate root cause analysis, data profiling, and rule suggestions.
• Collaboration and Documentation
  • Partner with Data engineers, Architect team, Cloud support team and business stakeholders to translate requirements into technical solutions.
  • Document DQ frameworks, ML workflows,…