Corporate Sector - Lead Data Engineer

JPMorganChase•Houston, TX

About The Position

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. You will deliver data collection, storage, access, and analytics platform solutions in a secure, stable, and scalable way, with clear SLOs/SLAs and operational readiness gates.

Requirements

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

Nice To Haves

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.

Responsibilities

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 NoSQL 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)