In this role, you will:

• Design Pega NBA source datasets for customer profile, interaction history, offers/eligibility, and outcomes—optimised for low-latency decisioning and explainability.
• Define canonical data models (facts/dimensions) for interactions and responses, including event taxonomy, identifiers, and channel standardisation.
• Implement history/auditability patterns (SCD Type 2, bitemporal where needed) to support regulatory traceability and “why did we decide X?” replay.
• Build near-real-time ingestion pipelines (CDC/streaming/micro-batch) with deterministic ordering, idempotency, and deduplication for event feeds.
• Establish data quality controls (completeness, validity, timeliness, consistency) with automated checks, thresholds, and exception workflows.
• Set up monitoring & observability for SLAs/SLOs (freshness, lag, volume anomalies), lineage, and alerting integrated with incident management.
• Partner with stakeholders (Pega decisioning, product, risk/compliance, analytics, engineering) to align eligibility rules, consent, and governance.
• Drive continuous improvement on performance, cost, and reliability—capacity planning, schema evolution, and controlled releases with rollback plans.

To be successful in this role, you should meet the following requirements:

• Strong experience designing decisioning/marketing/next-best-action datasets (customer, interactions, offers, outcomes) for operational use.
• Proven dimensional modelling skills (star schemas, conformed dimensions) plus event modelling for high-volume interaction data.
• Hands-on expertise with SCD Type 2 and audit patterns (effective dating, versioning, bitemporal concepts) for traceability.
• Experience delivering near-real-time data feeds (Kafka/Kinesis/PubSub, CDC tools, micro-batching) with exactly-once/at-least-once handling strategies.
• Strong data quality engineering background: automated validation frameworks, anomaly detection, reconciliation, and data contracts.
• Solid understanding of data governance: consent/permissions, PII handling, retention, and access controls in regulated environments.
• Proficiency in SQL and data engineering tooling (ETL/ELT, orchestration, CI/CD, schema registry, metadata/lineage).
  • Ability to communicate clearly with technical and non-technical teams; comfortable translating business rules into data requirements and SLAs.