Data Architect

Job purpose:

The Technical architect oversees design and own a scalable, secure, and unified data architecture that supports real-time and batch data movement, analytics across the retail banking segment. Build robust data ingestion and processing layers using modern platforms. Hands-on experience designing data architectures leveraging event enrichment, stateful stream processing, and windowing to enable real‑time customer & transactions data, and decisioning use cases across digital banking platforms. Enable seamless data integration from diverse enterprise data sources into ready to use digital channel databases or omnichannel, and event-driven architectures. Define data standards, governance,policy enforcement layer to control who can access what data, through which purpose and architectural best practices to ensure data quality, reliability, and regulatory compliance. Partner with engineering, analytics, and business teams to deliver high-performance, future-ready data solutions that drive insights and innovation.

Knowledge, Skills:

• At least 12+ years of experience delivering data-intensive solutions (data technical architect and engineering).
• Streaming and event driven data experience building and utilizing Kafka or queue-based architectures including ordering, replay, schema evolution, and real time observability.
• Strong hands-on expertise in Apache Kafka ecosystem including Kafka Connect, Kafka Streams (KStreams), ksqlDB, schema registry, producer/consumer services using Java.
• Strong understanding of transactional integrity, idempotency, and error handling in financial data flows. Hands-on experience building Java/Spring Boot–based data processing services that produce and consume Kafka events, apply real‑time transformations and enrichment, and persist channel‑optimized views into MongoDB to support low‑latency omni‑channel banking use cases.
• Experience designing replay safe and resilient consumers for banking workloads.
• Proven experience implementing and architecting Change Data Capture (CDC) data from heterogeneous sources (RDBMS, NoSQL, SaaS) using tools like Debezium, Goldengate, or native connectors.
• Solid understanding of MongoDB, Schema design & document modeling, Indexing, sharding techniques and performance tuning. Good to have experience in Azure cosmos for mongo db vCore architecture.
• Hands-on experience designing MongoDB as a real‑time, read‑optimized data serving layer, decoupled from systems of record, for mobile and omni‑channel banking use cases.
• Performance tuning for low‑latency mobile workloads.
• Hands-on expertise in architecting omni‑channel and Customer 360 data platforms using MongoDB or similar NoSQL databases. Experience building event‑driven materialized views in MongoDB to support mobile apps, web.
• Strong knowledge of stream and batch processing frameworks such as Spark, Flink, and cloud-native/on-prem data processing services.
• Expertise in modern data architecture including data mesh, and unified data platforms across on‑prem and cloud (Azure/AWS/GCP).
• Strong knowledge of API‑led and event‑driven architectures working together (REST + async events). Ability to define contracts, versioning, backward compatibility, and domain events aligned to banking domains.
• Hands-on experience building Java / Spring Boot services that consume Kafka events and persist real‑time, replay‑safe updates into MongoDB.
• Strong understanding of API‑driven architectures and how they integrate with streaming and event platforms.