Sr. Lead Data Engineer

JPMorgan Chase & Co.; Greater London; On-site; GBP 120,000 - 165,000; Full time

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.

Skills

Strong SQL

Data storytelling

Leadership

Dashboard development

AI-assisted data engineering

Education

Formal training or certification in data engineering

Tools

Tableau

Power BI

Looker

ServiceNow

AWS

dbt

Snowflake

Redshift

BigQuery

Job description

Embrace this pivotal role as an essential member of a high performing team dedicated to reaching new heights in data engineering. Your contributions will be instrumental in shaping the future of one of the world’s largest and most influential companies.

As a Senior Lead Data Engineer at JPMorganChase within the Behavioral Insights Team, you will turn operational signals and platform data into actionable insights that improve reliability, risk/control health, and delivery efficiency. You will own the reliability and performance of reporting and analytics pipelines, define and govern SLIs/SLOs and error budgets, and automate data products (dashboards, scheduled reporting, and near-real-time views) that serve engineering, risk, and business stakeholders. You will also manage and mentor team members and uphold rigorous data management practices and controls.

Job Responsibilities

• Change and release health - Track deployment frequency, change failure rate, lead time, 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 (e.g., cost per transaction), rightsizing opportunities, commitment utilization, and tag compliance; highlight reliability–cost tradeoffs.
• Risk and controls compliance - Evidence guardrail adherence and control health (backup/restore posture, DR testing, patch/vulnerability closure, config drift); ensure metrics lineage and audit readiness.
• Uses enterprise-authorized AI capabilities within the work environment to accelerate data platform and design analysis and technical documentation, validating outputs and handling data according to sensitivity and security requirements.
• Data risk and controls - Monitor adherence to risk and control guidelines for data access and use.
• Data quality, reliability, and lineage - Define data contracts for telemetry sources; implement validation, anomaly detection, and reconciliation; document definitions (e.g., formulas, thresholds) and end-to-end data lineage from raw signals to KPIs and insights.
• Automation and self‑service - Deliver automated pipelines for scheduled reporting and near-real-time dashboards; enable RBAC‑controlled self‑service for teams and leadership.
• Stakeholder cadences and communication - Lead weekly reliability reviews and monthly leadership reviews; maintain action logs to closure; escalate risks early with data‑driven recommendations.
• People leadership - oversee workflow, prioritization, and delivery for junior data engineers and visualization researchers; mentor and support upskilling and career development.
• Applies reuse-first, AI-assisted practices within delivery and operational routines (e.g., validation automation and access control review support), ensuring traceability/auditability and alignment to resiliency and security expectations.

Required qualifications, capabilities, and skills

• 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).
• Demonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity.
• Ability to review and validate AI-assisted outputs (e.g., model and design summaries or validation recommendations) before use, escalating when uncertain and following data handling requirements.
• Hands-on experience building dashboards in Tableau/Power BI/Looker (or similar).
• Experience working with ITSM tools (e.g., ServiceNow or similar) and understanding incident/change/problem concepts.
• Strong data storytelling skills; ability to translate operational findings into practical improvements.

Preferred qualifications

• Experience working with AWS and core concepts (accounts, regions, IAM, networking, compute/storage, tagging).
• Python for analytics/automation (e.g., pandas) and building repeatable pipelines.
• Experience with cloud data platforms (e.g., Snowflake/Redshift/BigQuery) and ELT tooling (e.g., dbt).