Senior Data Engineer Lead: Reliability & AI Analytics

JPMorgan Chase & Co.

Greater London

On-site

GBP 120,000 - 165,000

Full time

14 days+

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Job summary

JPMorgan Chase & Co. is seeking a Senior Lead Data Engineer within the Behavioral Insights Team to turn operational signals and platform data into actionable insights that improve reliability, risk and delivery efficiency.

You will own the reliability and performance of reporting and analytics pipelines, define SLIs/SLOs and automate data products that serve engineering, risk and business stakeholders. You will mentor junior engineers, uphold rigorous data governance, and apply AI-assisted

Qualifications

Formal training or certification on data engineering concepts and advanced applied experience in data analytics/BI/ operations analytics.
Strong SQL skills (CTEs, window functions, performance-aware querying).
Experience using enterprise-authorized AI capabilities with validation habits and awareness of data sensitivity.
Ability to review and validate AI-assisted outputs before use.
Hands-on experience building dashboards in Tableau/Power BI/Looker (or similar).
Experience with ITSM tools (e.g., ServiceNow) and understanding incident/change/problem concepts.
Strong data storytelling skills; translate operational findings into practical improvements.

Responsibilities

Change and release health — track deployment frequency, lead times, and rollbacks; correlate changes to incidents/SLO impact; influence safer release practices.
Capacity, performance, and scalability — produce capacity forecasts, headroom and hotspot reporting; partner with engineering to validate scaling policies and performance budgets.
FinOps and cost observability — report spend by service/team/env; track unit economics, rightsizing opportunities, and tag compliance.
Risk and controls compliance — evidence guardrail adherence and control health; ensure metrics lineage and audit readiness.
Uses enterprise-authorized AI capabilities to accelerate data platform and design analysis with strong validation and security considerations.
Data risk and controls — monitor adherence to data access and use guidelines.
Data quality, reliability, and lineage — define data contracts for telemetry sources; implement validation and end-to-end lineage.
Automation and self‑service — deliver automated pipelines for scheduled reporting and near-real-time dashboards; enable RBAC-controlled self-service.
Stakeholder cadences and communication — lead weekly reliability reviews and monthly leadership reviews; maintain action logs to closure.
People leadership — oversee workflow, prioritization, and delivery for junior data engineers and visualization researchers.
Applies reuse-first, AI-assisted practices ensuring traceability and alignment to resiliency and security.