Data Engineer (Contingent)

• Implement Definition-of-Done Quality Checks: Implement quality checks for each domain: equivalence and parity tests against systems of record and, where applicable, prior platforms; freshness SLAs and anomaly detection tied to pipeline events, publishing pass and fail results and residual risk notes in release artifacts.

• Instrument Observability: Wire pipelines to platform monitoring and defect tracking, with playbooks for alerting, triage, rollback, and hot-fix.

• Document Ingestion and Modeling Choices: Document pattern, orchestration, lineage, and tuning guidance, and keep links to approvals and decisions current through decision tickets and Architecture Decision Records.

• Configure Federated Query Tooling: Configure and implement federated query tooling in alignment with the policies and targets set by the data governance workstream.

• Provide Solution Consulting and Provisioning: Assess customer data, analytics, and operational requirements and recommend fit-for-purpose data architecture and ETL solutions. Guide the provisioning of required data infrastructure, tooling, and pipelines aligned with approved enterprise standards.

• Define and Monitor KPIs: Define and monitor performance and cost KPIs such as timed loads, parallelism, data quality, and storage and egress for chosen patterns, and publish tuning recommendations for domain teams, documented in a Data Quality and Ingestion Dashboard.