AVP Software Engineer - Data Platform Engineering (Python/AWS)

Where Ambition Meets Innovation

Build a career that matches all your initiative with an impressive dose of innovation. From cutting-edge resources and a collaborative environment to the freedom to make an impact and more, you’ll find the ingredients you need at LPL Financial to shape your success while helping clients pursue their financial goals.

Job Overview:

This is a unique opportunity to help build and scale a strategic platform at the center of LPL's modern data ecosystem. The team is responsible for batch integration, data sourcing, data movement, and delivering trusted data products to downstream consumers across the enterprise.

As an AVP Software Engineer in Data Technology, you will play a key role in designing and developing highly scalable backend services, data processing capabilities, and monitoring solutions that support complex enterprise data pipelines. You will help build innovative capabilities to monitor, visualize, and analyze large-scale data flows while enabling intelligent detection of data quality issues, operational anomalies, and pipeline failures.

This role sits within the Connectics team, which is responsible for enterprise data integration, backend services, APIs, batch ingestion, and making trusted data assets available to downstream consumers across LPL's modern data platform. The primary focus is on building scalable Python-based services, integration frameworks, and cloud-native applications that enable data movement, access, and consumption across the organization.

The ideal candidate is a strong Python backend engineer with deep experience building data-intensive applications, cloud-native solutions, and modern data platform capabilities on AWS. While the role is primarily backend-focused, experience developing user interfaces with React is desirable.

Responsibilities:

• Design, develop, and maintain scalable Python-based backend services supporting enterprise data integration, enrichment, validation, and distribution.

• Build and enhance data pipeline frameworks supporting batch ingestion, transformation, orchestration, and downstream data consumption.

• Develop cloud-native applications leveraging AWS serverless technologies including Lambda, DynamoDB, API Gateway, EventBridge, SQS, and Step Functions.

• Develop reusable integration patterns that simplify connectivity between source systems, data platforms, and consuming applications.

• Improve observability, monitoring, and operational resilience across data pipelines and integration services.

• Leverage AI-assisted development tools and modern engineering practices to improve code quality, productivity, troubleshooting, and documentation.