Senior Data Architect

• Data Products (To-Be): Channel Ops Warehouse (~30-day high-perf layer) and Channel Analytics Lake (7+ yrs). Expose status and statements APIs with clear SLAs. • Platform Architecture: S3/Glue/Athena/Iceberg lakehouse, Redshift for BI/ops. QuickSight for PO/ops dashboards. Lambda/Step Functions for stream processing orchestration. • Streaming & Ingest: Kafka (K4/K5/Confluent) and AWS MSK/Kinesis; connectors/CDC to DW/Lake. Partitioning, retention, replay, idempotency. EventBridge for AWS-native event routing. • Event Contracts: Avro/Protobuf, Schema Registry, compatibility rules, versioning strategy. • As-Is → To-Be: Inventory APIs/File/SWIFT feeds and stores (Aurora Postgres, Kafka). Define migration waves, cutover runbooks. • Governance & Quality: Data-as-a-product ownership, lineage, access controls, quality rules, retention. • Observability & FinOps: Grafana/Prometheus/CloudWatch for TPS, success rate, lag, spend per 1M events. Runbooks + actionable alerts. • Scale & Resilience: Tens of millions of payments/day, multi-AZ/region patterns, pragmatic RPO/RTO. • Security: Data classification, KMS encryption, tokenization where needed, least-privilege IAM, immutable audit. • Hands-on Build: Python/Scala/SQL; Spark/Glue; Step Functions/Lambda; IaC (Terraform); CI/CD (GitLab/Jenkins); automated tests. Must-Have Skills: • Streaming & EDA Kafka (Confluent) and AWS MSK/Kinesis/Kinesis Firehose; outbox, ordering, replay, exactly/at-least-once semantics. EventBridge for event routing and filtering. • Schema Management: Avro/Protobuf + Schema Registry (compatibility, subject strategy, evolution). • AWS Data Stack: S3/Glue/Athena, Redshift, Step Functions, Lambda; Iceberg-ready lakehouse patterns. Kinesis→S3→Glue streaming pipelines; Glue Streaming; DLQ patterns. • Payments & ISO 20022: PAIN/PACS/CAMT, lifecycle modeling, reconciliation/advices; API/File/SWIFT channel knowledge. • Governance: Data-mesh mindset; ownership, quality SLAs, access, retention, lineage. • Observability & FinOps: Build dashboards, alerts, and cost KPIs; troubleshoot lag/throughput at scale. • Delivery: Production code, performance profiling, code reviews, automated tests, secure by design. • Data Architecture Fundamentals (Must-Have): - Logical Data Modeling Entity-relationship diagrams, normalization (1NF through Boyce-Codd/BCNF), denormalization trade-offs; identify functional dependencies and key anomalies. - Physical Data Modeling Table design, partitioning strategies, indexes; SCD types; dimensional vs. transactional schemas; storage patterns for OLTP vs. analytics. - Normalization & Design Normalize to 3NF/BCNF for OLTP; understand when to denormalize for queries; trade-offs between 3NF, Data Vault, and star schemas. - CQRS (Command Query Responsibility Segregation) Separate read/write models; event sourcing and state reconstruction; eventual consistency patterns; when CQRS is justified vs. overkill. - Event-Driven Architecture (EDA) Event-first design; aggregate boundaries and invariants; publish/subscribe patterns; saga orchestration; idempotency and at-least-once delivery. - Bounded Contexts & Domain Modeling Core/supporting/generic subdomains; context maps (anti-corruption layers, shared kernel, conformist, published language); ubiquitous language. - Entities, Value Objects & Repositories Domain entity identity; immutability for value objects; repository abstraction over persistence; temporal/versioned records. - Domain Events & Contracts Schema versioning (Avro/Protobuf); backward/forward compatibility; event replay; mapping domain events to Kafka topics and Aurora tables. Nice-to-Have: - QuickSight/Tableau; Redshift tuning; ksqlDB/Flink; Aurora Postgres internals. - Edge/API constraints (Apigee/API-GW), mTLS/webhook patterns.