Sr. Lead Data Engineer

Job Descriptionhackajob is collaborating with J.P. Morgan to connect them with exceptional professionals for this role. NJOB DESCRIPTION NEmbrace 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. NAs a Senior Lead DataEngineer 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. NJob Responsibilities NnChange and release health – Track deployment frequency, change failure rate, lead time, and rollbacks; correlate changes to incidents/SLO impact; influence safer releasepractices. NCapacity, performance, and scalability – Produce capacity forecasts, headroom and hotspot reporting; partner with engineering to validate scaling policies and performance budgets. NFinOps 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. NRisk 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. NUses 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. NData risk and controls – Monitor adherence to risk and control guidelines for data access and use. NData 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. NAutomation and selfâservice – Deliver automated pipelines for scheduled reporting and nearârealâtime dashboards; enable RBACâcontrolled selfâservice for teams and leadership. NStakeholder cadencesand communication – Lead weekly reliability reviews and monthly leadership reviews; maintain action logs toclosure; escalate risks early with dataâdriven recommendations. NPeople leadership – oversee workflow, prioritization, and delivery for junior data engineers and visualization researchers; mentor and support upskilling and career development. NApplies 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. NnRequired qualifications, capabilities, and skills NnFormal training or certification on data engineering concepts and advanced applied experience in data analytics/BI/ operations analytics. NStrong SQL skills (CTEs, window functions, performance-aware querying). NDemonstrated experience using enterprise-authorized AI capabilities within the work environment to support data engineering workflows with strong validation habits and awareness of data sensitivity. NAbility 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. NHands-on experience building dashboards in Tableau/Power BI/Looker (or similar). NExperience working with ITSM tools (e.G., ServiceNow or similar) and understanding incident/change/problem concepts. NStrong data storytelling skills; ability to translate operational findings into practical improvements. NnPreferred qualifications NnExperience working with AWS and core concepts (accounts, regions, IAM, networking, compute/storage, tagging). NPython for analytics/automation (e.G., pandas) and building repeatable pipelines. NExperience with cloud data platforms (e.G., Snowflake/Redshift/BigQuery) and ELT tooling (e.G., dbt). NnABOUT USnJ.P. Morgan is a global leader in financial services, providing strategic advice and products to the world’s most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives. NWe recognize that ourpeople are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our NFAQsnfor more information about requesting an accommodation. NABOUT THE TEAMnJ.P. Morgan’s Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world….