Sr. Director of Software Engineering
Key Skills for This Role
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Job Responsibilities
- Drives Snowflake-based analytics/AI enablement with governed data products, secure access, scalable patterns, and performance and cost guardrails.
- Defines reference architectures and reusable components with engineering and platform teams (e.g., payments-scale aggregation, merchant entity resolution). Drives delivery excellence through agile practices, milestone execution, and rigorous dependency management.
- Operates a production-grade SDLC for data/AI products: CI/CD, automated testing, observability, runbooks, incident response, and rollback.
- Owns the Merchant Payments analytics and AI roadmap aligned to measurable outcomes (e.g., TPV growth, authorization uplift, fraud and chargeback loss reduction, onboarding cycle time reduction, merchant experience).
- Delivers analytics products end-to-end (dashboards and KPIs, self-serve analytics, predictive and decision support) with adoption and lifecycle ownership.
- Sets and enforces code quality and quality gates (reviews, static analysis, secure coding, unit and integration testing). Drive timely delivery through strong execution mechanics (milestones, dependency management, CI/CD automation, release discipline).
- Sponsors governance across ownership and stewardship, metadata and lineage, data quality SLAs, retention and access controls, and dataset certification. Standardize critical metric definitions to reduce drift and reconciliation (e.g., TPV, authorization rate, losses, disputes and chargebacks).
- Run governance forums and issue and remediation processes aligned to delivery priorities. Enables sales and business teams with timely, trusted insights that drive targeting, retention, and portfolio actions.
- Leads multi-disciplinary teams (analytics, data science, product, engineering, governance) including hiring, coaching, and performance management. Manages budget and capacity planning, partner engagement as needed, and ROI and value tracking tied to outcomes.
- Sets direction and governance for agentic AI-enabled engineering and SDLC/TLM automation within a technical area to drive measurable improvements in speed, quality, and operational outcomes (e.g., AI-orchestrated delivery workflows, release readiness controls, automated test modernization, and incident triage acceleration), while establishing guardrails for validation, security, resiliency, traceability, and reuse across teams.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation and support capacity unlock initiatives at scale.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 10+ years applied experience. In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise
- Track record delivering enterprise analytics and AI solutions end-to-end, from intake and roadmap to production, adoption, and ongoing operations.
- Demonstrated engineering leadership with strong SDLC discipline (code reviews, automated testing, CI/CD, release governance).
- Ownership of non-functional requirements for business-critical platforms (availability, resiliency, performance, observability, security).
- Strong understanding of modern data platforms and governance (data products, metadata and lineage, data quality, access controls).
- Business value delivered (revenue growth, cost reduction, risk and loss reduction). Adoption and satisfaction of analytics, AI, and self-serve capabilities.
- Delivery and reliability (time-to-market, availability and SLO attainment, incident rate and MTTR). Data trust and governance (quality SLAs, certified datasets, lineage and metadata coverage, access turnaround).
- Agent performance and audit readiness (task success rate, evaluation results, incident rate, control effectiveness).
- Executive stakeholder management across Technology, Product, Sales, Operations, and Risk/Controls, with the ability to translate strategy into measurable outcomes.
- Experience leading adoption of agentic AI-enabled engineering practices (using enterprise-authorized tools within the work environment) across teams, including defining operating expectations (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs.
- Strong understanding of responsible AI use and control expectations in engineering workflows, including data sensitivity, resiliency/security implications, and governance; ability to influence leaders on safe scaling patterns and reuse.
Preferred qualifications, capabilities, and skills
- Hands-on Snowflake experience for scalable analytics and AI workloads, including performance and cost optimization patterns.
- Experience building and operating LLM and agentic systems with evaluation, safety controls, monitoring, and human oversight.
- Experience operating high-throughput, low-latency, 24x7 platforms (payments strongly preferred).
- Experience in regulated environments and merchant payments familiarity (authorization performance, disputes and chargebacks, fraud and loss, onboarding and KYC, servicing workflows).
About JPMorgan Chase
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