Job Duties and Responsibilities:

• Architect enterprise-grade data solutions using Model N, Snowflake, Databricks enabling real-time query resolution across structured, semi-structured, and unstructured data sources.
• Lead design and implementation of cloud-native microservices-based data mesh architecture, facilitating decentralized data ownership and scalability across Rebate and Payment Systems.
• Develop data integration blueprints incorporating streaming ingestion pipelines, ETL/ELT frameworks, and no-code connectors, supporting rapid onboarding of regulatory and transactional datasets.
• Design custom inference pipelines leveraging AI/ML models and graph traversal logic, enabling proactive detection of suspicious transaction patterns and enhancing anti-fraud frameworks.
• Deliver solution architectures integrating Model N, Datahub with third-party systems (e.g., Snowflake, Databricks, Splunk, Palantir), ensuring seamless data federation and actionable insight generation across fragmented data ecosystems.
• Author comprehensive architectural artifacts data flow diagrams, sequence diagrams, and API schemas used by internal and client-side development teams across high-impact financial engagements.
• Maintain architecture documentation: design artifacts, data dictionaries, API specs, ontology models, and governance policies.
• Collaborate in maintaining an enterprise metadata strategy, ensuring interoperability with data catalogs and compliance tools.
• Oversee technical teams (data engineers, developers, integrators) during deployment to ensure adherence to architecture, scalability, resilience, performance, and security standards.
• Mentor and guide other architects or engineering teams on best practices around knowledge graph modeling, microservices orchestration, and cloud deployment.
• Digital Strategy & Transformation with deep expertise across Business, System, Data, and Integration Architectures. Designing and modernizing enterprise platforms using the latest Cloud and AI technologies, including Snowflake, Airflow, Databricks, Azure, AWS, Informatica Cloud, iPaaS, Workato, Microsoft Fabric, and Power BI.

Technologies / Environment involved:

• Distributed storage: AWS Cloud Storage (S3), Azure, Google Cloud (GCP)
• Database management: SQL Server, Snowflake, DataBricks, Oracle, Big Data Factory
• Machine learning: Spark Machine Learning Library (MLlib), TensorFlow, Keras, Pytorch
• Data processing: Spark, Hadoop MapReduce, Kafka and Storm, Airflow, Spark-streaming
• Programming Languages: Java, Scala, Python, PySpark
• DevOps Tools: Git, Apache Maven, Selenium, Jenkins, Docker
• Revenue Management: Model N Flex, Powered by N, Vistex, Aptus
• Data Integration: dbt, Informatica, Azure Data Factory, Microsoft Fabric
• Reporting: Power BI, Cognos, Tableau