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.

Education

Master Of Engineering,Bachelor of Engineering