Job Duties and Responsibilities:

• Architect enterprise-grade data solutions for financial institutions using DataBlaze’s AI-powered knowledge graph, 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 KYC, AML, and fraud detection systems.
• Develop data integration blueprints incorporating streaming ingestion pipelines, ETL/ELT frameworks, and no-code connectors, supporting rapid onboarding of regulatory and transactional datasets.
• Engineer semantic ontologies and metadata models aligned with FINRA, OCC, and Basel III requirements, enabling automated compliance reporting and lineage tracking within complex multi-jurisdictional environments.
• Design custom inference pipelines leveraging AI/ML models and graph traversal logic, enabling proactive detection of suspicious transaction patterns and enhancing anti-fraud frameworks.
• Collaborate with cross-functional teams—including compliance officers, data scientists, and DevOps engineers to deploy resilient, high-throughput data services on AWS, Azure and GCP, achieving 99.99% availability.
• Deliver solution architectures integrating DataBlaze 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.
• Spearhead customer architecture workshops and proof-of-concept implementations for global Tier 1 Banks/Financial Institutions, contributing to enterprise adoption of knowledge graph–driven data governance.

Technologies / Environment involved:

• Distributed storage: AWS Cloud Storage (S3), Azure HD Insight, Google Cloud (GCP)
• Database management: Mongo DB, Cassandra, Postgres, Oracle, MS SQL Server, Redshift
• Graph Processing: Neo4J
• 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 [REST Framework], PySpark
• DevOps Tools: BitBucket, Git, Apache Maven, Selenium, Jenkins, Docker