At LPL’s Global Capability Center, you'll find a collaborative culture where your voice matters, integrity guides every decision, and technology fuels progress. Your skills, talents, and ideas will redefine what's possible. LPL's success reflects its exceptional employees, who together pursue one noble purpose: empowering financial advisors to deliver personalized advice for all who need it. We’re proud to be expanding and reaching new heights in Hyderabad.
Join us as we create something extraordinary together.
AVP, Software Engineering — Direct Business & LDX | LPL Technologies
Why this role
We are seeking a highly skilled AVP to join our
Direct Business and LPL Data Experience (LDX) team. This role is critical to designing and implementing modern, cloud‑based data and application capabilities that will transform how Direct business data is injested, managed and leveraged for various business functionalities within the firm for advisors, investors cross the enterprise. This role is critical to designing and implementing modern, cloud‑based data and application capabilities that will transform how compensation processing, data flows, validation, and automation operate across the enterprise.
The ideal candidate will bring strong experience in Angular or REACT with Python, AWS and .NET API development with strong SQL database experience.
You will work closely with architects, product teams, compensation SMEs, and engineering partners to deliver scalable, resilient, and future‑ready systems that modernize the compensation platform end‑to‑end.
What you will own
- Leadership: Build, coach, and develop engineers; set a culture of ownership, craftsmanship, and customer empathy. Hire and grow leaders.
- Modernization: Drive a pragmatic roadmap to improve architecture, reduce risk, retire debt, and migrate toward durable cloud patterns—without “big bang” disruption to the business.
- Quality at scale: Establish engineering standards for reliability, security, observability, testing, and operational readiness in business-critical systems.
- Cross-functional partnership: Work tightly with Product, Architecture, Platform, Security, Operations, and business stakeholders to prioritize outcomes, manage tradeoffs, and communicate clearly upward and outward.
- AI, two ways:
- For customers and operations: identify high-value workflows (onboarding, exceptions, validations, investigations, decision support) where intelligent automation improves speed, accuracy, and auditability—within LPL governance.
- For engineering: accelerate delivery responsibly (design assistance, code/test/docs workflows) with standards that protect quality, security, and compliance.
What you will bring (hands-on to lead credibly) You are expected to be technically credible with complex enterprise systems—even as your primary job is leading the team. You should be comfortable diving into architecture reviews, critical incidents, and the hardest design decisions.
Must-have
- 10+ years software engineering experience in enterprise environments; 5+ years leading engineering teams (managers and senior ICs).
- Track record shipping and operating mission-critical applications; experience in regulated / financial services strongly preferred.
- Deep experience with .NET / C# and cloud-native engineering on AWS (compute, data, messaging/integration, identity patterns, observability).
- Strong SQL / relational data modeling, performance, and operational data concerns; comfort with Python for automation, services, or data/AI-adjacent workloads.
- Demonstrated success leading modernization programs (incremental migration, platform patterns, service boundaries, strangler approaches) while keeping production stable.
- Proven ability to introduce AI-enabled capabilities and/or AI-accelerated engineering with measurable impact and appropriate controls.
Strongly preferred
- Domain familiarity with advisor/practice onboarding, compensation, registration/licensing workflows, or fee/billing platforms.
- Experience with distributed systems, APIs, event-driven patterns, and pragmatic microservices where they earn their complexity.
- Background shaping CI/CD, automated testing strategy, SRE practices, incident learning, and engineering metrics.
- Familiarity with model/workflow governance, monitoring, and responsible AI practices in enterprise settings.
How you work Systems thinking, decisive prioritization, calm under ambiguity, and executive-ready communication. You raise the bar by clarifying standards, not by adding process for its own sake.
What success looks like in year one
- A healthier technical foundation: clearer ownership, better observability, fewer repeat incidents, faster safe releases.
- A team that attracts and retains strong engineers—and ships modernization milestones on a credible cadence.
- A small set of high-confidence AI wins (operations and/or product) with documented guardrails and measurable outcomes.
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