Corporate - Lead Data Engineer

Join a team that builds secure, scalable data pipelines and platforms for collection, access and analytics.

As a Lead data Engineer, within the Corporate Sector you will lead architecture and engineering for scalable backup, recovery, immutable-data protection, and recovery-assurance services across data platforms and storage tiers and deliver data collection, storage, access, and analytics platform solutions in a secure, stable, scalable way, with clear SLOs/SLAs and operational readiness gates.

• Design, build, and maintain role-based observability dashboards for data platforms and backup/recovery services using Grafana, Dynatrace, and Tableau
  , providing real-time and historical visibility into platform health, performance, and risk posture.
• Define and govern dashboard standards and operating model (golden dashboards, drill-down paths, naming/tagging conventions, ownership, and lifecycle management) to drive consistent adoption across Engineering, SRE, Operations, and Risk.
• Ensure end-to-end instrumentation and telemetry quality so dashboards surface actionable signals (availability, latency, throughput, error rates, saturation) and platform KPIs (job success/failure, lag/backlog, data freshness, restore success, retention coverage).
• Establish and publish SLIs/SLOs and operational health KPIs; translate these into Grafana/Dynatrace visualizations and Tableau reporting for executive and governance stakeholders.

• Generate and govern data models using firmwide tooling; apply linear algebra, statistical, and geometric algorithms where relevant for modeling and optimization.
• Own and continuously improve the strategy for database backup, recovery, archiving, retention, and restore testing across relational and No
  
  SQL estates.

• Drive an automation-first, API-led, self-service approach that reduces operational toil and improves resilience and customer experience.
• Build cloud-native capabilities using AWS services
  , infrastructure as code
  , CI/CD
  , and modern software engineering practices.

• Embed SRE principles by defining and tracking reliability metrics (
  SLIs/SLOs
  ), implementing observability standards, leading incident learning, and maintaining runbooks and recovery playbooks
  .
• Participate in an on-call and incident leadership rotation
  , acting as an escalation point for complex platform and data reliability issues.

• Assess and report on access control effectiveness and data asset security posture
  ; partner with security/risk to remediate gaps.
• Design preventive/detective controls, policy guardrails, automated validation, and audit-ready evidence for platform controls and recovery readiness.
• Deliver telemetry, reporting, and operational intelligence for backup health, compliance, and recovery assurance (coverage, success rates,
  RPO/RTO attainment).

• Set engineering standards, perform high-quality code reviews, mentor engineers, and influence stakeholders across product, architecture, security, and SRE.
• Coordinate cross-team delivery with explicit dependencies, milestones, and measurable outcomes; manage technical debt and balance reliability/security work alongside feature delivery.

• Data architecture & modeling (lakehouse/warehouse patterns, dimensional modeling, data contracts)
• Advanced SQL (performance tuning, warehousing design)
• Pipeline engineering & orchestration (reliable batch workflows, backfills, SLAs)
• Distributed processing (e.g., Spark; partitioning, joins/shuffles, file formats)

• Data quality & testing (automated checks: schema/freshness/volume/business rules)

• Typically8+ years
  of applied engineering experience (software engineering or related discipline), including leading
  production-critical systems
  ; experience with both
  relational and No
  
  SQL
  databases;
  Strong capability in: observability & operations (monitoring, lineage, incident response/runbooks), security/privacy/governance (least privilege, encryption concepts, auditability), system design & tradeoff analysis (scalability, latency, reliability, cost), and technical leadership (standards, mentoring, stakeholder alignment).
• Proficient across the data lifecycle (ingestion, modeling, storage, serving, governance,…