Corporate Sector - 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.

Job Responsibilities:

- Generate and govern data models for the team using firmwide tooling; apply linear algebra, statistical, and geometrical algorithms where relevant for modeling/optimization and own and continuously improve database backup, recovery, archiving, retention, and restore testing strategy across relational and NoSQL estates.
- Impact an automation-first, API-led, self-service approach that reduces operational toil and improves resilience and customer experience amd build cloud-native capabilities using AWS services, infrastructure as code, CI/CD, and modern software engineering practices.
- Embed SRE principles: define and track reliability metrics (SLOs/SLIs), implement observability standards, lead incident learning, and maintain runbooks and recovery playbooks and evaluate and report on access control effectiveness and data asset security posture with minimal supervision; partner with security/risk to close gaps.
- Design preventive/detective security controls, policy guardrails, automated validation, and audit-ready evidence for platform controls and recovery readiness and deliver telemetry, reporting, and operational intelligence for backup health, compliance, and recovery assurance (coverage, success rates, RPO/RTO attainment).
- Provide technical leadership: set engineering standards, perform high-quality code reviews, mentor engineers, and influence senior stakeholders across product, architecture, security, and SRE.
- Coordinate cross-team delivery with explicit dependencies, milestones, and measurable outcomes; manage technical debt and prioritize reliability/security work alongside feature delivery and participate in an on-call/incident leadership rotation as needed, acting as an escalation point for complex platform and data reliability issues.
- 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), and data quality & testing (automated checks, schema/freshness/volume/business rules)

Required qualifications, capabilities, and skills:

- Typically 8+ years of applied engineering experience (software engineering or related discipline), including leading production-critical systems and working experience with both relational and NoSQL databases.
- Proficient across the data lifecycle (ingestion, modeling, storage, serving, governance, operations) and demonstrated experience building distributed systems, platform services, APIs, and automation frameworks.
- Strong knowledge of enterprise data protection, backup and recovery, cyber resilience, immutable storage, retention, and recovery testing and advanced AWS experience spanning backup/data protection services, IAM/security, networking basics, observability, and infrastructure automation.
- Hands-on experience with observability/monitoring platforms such as Dynatrace and Grafana and proficiency in Python and at least one additional language (e.g., Java, Go, C#).
- Experience with Terraform or CloudFormation and CI/CD platforms (e.g., Jenkins, GitLab, GitHub Actions) and experience implementing database backup, recovery, and archiving strategies with measurable RPO/RTO targets.
- Proficient knowledge of linear algebra, statistical, and geometrical algorithms and strong ability to translate security, control, and regulatory requirements into engineered solutions and operational processes.
- 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, code reviews, mentoring, stakeholder alignment)

Preferred qualifications, capabilities, and skills:

- Experience with Cohesity, Commvault, AWS Backup, or similar enterprise backup platforms.
- Experience with Kubernetes/OpenShift and containerized platform operations.
- Experience with database platforms and cyber-recovery/isolated recovery solutions.
- Experience building operational analytics (health/compliance dashboards, evidence automation) for regulated financial services environments.