Database Architect –RegTech-as-a-Service Product

As Database Architect, you will own the logical and physical database architecture for the RegTech-as-a-Service product. You will design the canonical data model, operational and analytical stores, metadata repositories, audit and lineage structures, and data-access patterns required across product modules.

You will work closely 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.

Responsibilities

• Define the end-to-end database architecture, technology choices and design standards for operational, analytical, metadata, audit, evidence and AI-ready data stores.
• Design a reusable, report-agnostic canonical data model covering regulatory entities, customers, accounts, transactions, balances, MDRMs, schedules, controls, exceptions, approvals and lineage.
• Create logical and physical data models, schemas, keys, relationships, indexing, partitioning, retention and archival strategies for 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 aligned with Secure-by-Design and Privacy-by-Design principles.
• Define ingestion, change-data-capture, API 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 including query optimization, capacity planning, workload management, observability, backup, recovery, high availability and disaster recovery.
• Design metadata-driven configuration for report mappings, validation rules, thresholds, regulatory changes, schedule dependencies and source-to-report lineage.
• Lead database design reviews, migration planning, data quality controls, proof-of-concepts and production-readiness assessments while mentoring engineers and maintaining 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 designing canonical models, 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 convert 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 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/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

Education

Master Of Engineering,Bachelor of Engineering