AWS Data Architect

Job Summary:

We are seeking a highly experienced Data Architect (Hands-On) to design and build the data backbone for Payments Data Products. This role is responsible for leading AWS lakehouse architecture, real-time streaming ingestion, data governance, observability, and operational excellence to support high-performance payment-status and analytics workloads.

You will architect, build, and optimize data products that support sub-10 ms payment-status lookups, real-time event ingestion, 7+ years analytical retention, and enterprise-grade governance.

Key Responsibilities

• Data Products & Architecture
  Own architecture for Channel Ops Warehouse (high-performance 30-day layer) and Channel Analytics Lake (7+ year retention).
• Deliver status APIs and statement APIs with strict SLAs.
  Architect AWS lakehouse using S3, Glue, Athena, Iceberg, and Redshift for BI/operational analytics.Build dashboards using QuickSight for operations insights.
• Streaming & Ingestion
  Design resilient pipelines using Kafka / MSK / Kinesis for ISO 20022 lifecycle events (VPM/PMN/PSN).Implement CDC, partitioning, retention, idempotency, replay, DLQ patterns, and event routing via EventBridge.
• Event Contracts
  Define event schemas using Avro/Protobuf, versioning strategies, and schema compatibility rules.
• Migration (As-Is To-Be)
  Inventory existing APIs, file feeds, SWIFT, Aurora Postgres, Kafka topics.Define migration plan, cutover waves, and runbooks.
• Governance & Quality
  Implement data-as-a-product practices, quality SLAs, lineage, retention rules, and access controls.
• Observability & FinOps
  Build dashboards (Grafana/Prometheus/CloudWatch) to track TPS, lag, throughput, success rate, and cost per 1M events.Publish runbooks, alerts, and budgets.
• Scale & Security
  Architect for tens of millions of payments/day with multi-AZ/region patterns.Implement encryption, tokenization, IAM least-privilege, and immutable audit trails.
• Hands-On Build
  Write production-grade code in Python / Scala / SQL.Build ETL/ELT in Spark/Glue; orchestrate workflows via Lambda / Step Functions.Implement IaC using Terraform and CI/CD using GitLab/Jenkins.Ensure automated testing and code reviews.

Must-Have Skills

• Kafka/MSK/Kinesis; EDA, event ordering, replay, idempotency.
• Avro/Protobuf + Schema Registry.
• AWS data stack: S3, Glue, Athena, Redshift, Lambda, Step Functions.
• Payments domain + ISO 20022 (PAIN/PACS/CAMT).
• Data governance, lineage, retention, access control.
• Observability & cost management (Grafana/Prometheus/CloudWatch).
• Production-grade coding (Python/Scala/SQL).
• Terraform, CI/CD, automated tests.
  

Data Architecture Fundamentals (Required)

• Logical & physical data modeling, normalization (up to BCNF), denormalization strategies.
• SCDs, partitioning, indexes, OLTP vs OLAP schema patterns.
• CQRS, event sourcing, eventual consistency.
• Event-driven design, bounded contexts, domain events.
• Schema evolution strategies and domain-driven design concepts.
  

Preferred Skills (Nice to Have)

ksqlDB, Flink, Aurora internals.
Redshift performance tuning.API gateways (Apigee/API-GW), mTLS, webhook patterns.