Database Architect –RegTech-as-a-Service Product

Job Description:

• As a Database Architect, you will take ownership of the logical and physical database architecture for the RegTech as a Service product
• You will design canonical data models across operational and analytical stores, including metadata repositories, audit and lineage structures, and data access patterns needed across product modules
• You will partner with product leadership, regulatory SMEs, solution architects, data engineers, backend developers, AI/ML engineers, security, DevOps, and QA teams to deliver a database foundation that is performant, reliable, secure, explainable, and configurable across reports, regulations, and client deployments

Key Responsibilities:

• Define end-to-end database architecture, including technology selections and design standards for operational, analytical, metadata, audit evidence, and AI-ready data stores
• Design a reusable, report-agnostic canonical data model that covers regulatory entities, customers, accounts, transactions, balances, MDRMs, schedules, controls, exceptions, approvals, and lineage
• Create logical and physical data models and schemas, including keys, relationships, indexing, partitioning, retention, and archival strategies to support high-volume regulatory and financial crime workloads
• Architect multi-tenant data isolation, role-based access, encryption, masking, audit logging, and jurisdiction-aware data residency controls in line with Secure by Design and Privacy by Design principles
• Define ingestion patterns, including change data capture, API-based access, and batch data access patterns for integrating core banking, general ledger reporting platforms, CRM, payment sanctions, and external regulatory data sources
• Establish database performance engineering practices covering query optimization, capacity planning, workload management, observability, backup and recovery, high availability, and disaster recovery
• Design metadata-driven configuration for report mappings, validation rules, thresholds, regulatory change scheduling, dependency management, and source-to-report lineage
• Lead database design reviews, migration planning, data quality controls, proof of concepts, and production readiness assessments; mentor engineers and maintain architecture documentation

Technical Requirements:

• Minimum 7 years of experience in database architecture, data architecture, data engineering, or enterprise-scale data platform delivery, including at least 6 years in an architect or technical leadership role
• Strong hands-on expertise in relational database design and advanced SQL, preferably PostgreSQL, Microsoft SQL Server, Oracle, or equivalent enterprise RDBMS platforms
• Proven experience building canonical models, including normalized and dimensional schemas; metadata-driven solutions; audit stores; temporal data; and high-volume transactional systems
• Strong knowledge of indexing, partitioning, query optimization, concurrency, data lifecycle management, backup and recovery, high availability, and disaster recovery
• Experience designing secure cloud database solutions on Azure, AWS, or GCP, including identity, encryption, secrets, network isolation, monitoring, and cost controls
• Strong understanding of data governance, lineage, data quality, privacy, access control, PII handling, and regulatory-grade auditability
• Ability to translate business and regulatory requirements into practical database designs and collaborate effectively with architecture, engineering, security, and functional teams

Additional Responsibilities:

• Experience in banking regulatory reporting, AML KYC, transaction monitoring, risk compliance, finance, or other data-intensive regulated platforms
• Exposure to Azure SQL, Azure Database for PostgreSQL, Cosmos DB, Databricks, Snowflake, BigQuery, cloud data lakes, or lakehouse architectures
• Working knowledge of NoSQL graph and vector technologies such as Cosmos DB, MongoDB, Neo4j, pgvector, Pinecone, Weaviate, or Milvus, with sound judgment on when each is appropriate
• Experience with Python, PySpark, dbt, Airflow, Kafka, schema registries, data catalogues, CDC tools, and automated database deployment using CI/CD and infrastructure as code
• Familiarity with MDRM-driven regulatory data, XBRL and XML outputs, source-to-report reconciliation controls, evidence, AI RAG retrieval patterns, and human review workflows

Preferred Skills:

Technology->AI-Generative AI->Generative AI - Basic->open-source models (meta llama 2),Technology->Virtualization->Database Virtualization