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

LPL Financial•Fort Mill, SC

1d•$127,100 - $211,900•Hybrid

About The Position

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.

Requirements

Minimum of 7 years of professional software engineering experience, including deep expertise in Python backend development and experience with React and modern JavaScript frameworks.
Minimum of 5 years designing and building scalable backend services, REST APIs, and distributed systems in enterprise environments.
Minimum of 5 years developing cloud-native applications on AWS, with hands-on experience building and supporting production workloads.
Strong experience with AWS serverless technologies including Lambda, API Gateway, DynamoDB, EventBridge, SQS, and related services.
Experience building modern data platform solutions, including batch processing, data integration, data ingestion, and enterprise data distribution pipelines.
Strong Python engineering fundamentals, including application design, testing, performance tuning, and operational support.
Working knowledge of React and modern JavaScript frameworks for developing operational dashboards and user interfaces.
Experience building and integrating RESTful APIs and event-driven architectures.
Experience with CI/CD pipelines, automated testing, DevOps practices, and cloud-native delivery models.
Experience working within modern data platforms leveraging technologies such as Snowflake, Airflow, Databricks, or similar ecosystems.
Understanding of data integration, data observability, monitoring, and operational resilience concepts.
Understanding of how modern data platforms support analytics, machine learning, and AI-enabled workloads.
Ability to operate effectively in ambiguous environments and solve complex technical challenges.
Experience mentoring engineers, conducting code reviews, and contributing to engineering best practices.
Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.

Nice To Haves

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.
Collaborate with Data, AI, and Analytics teams to expose data and metrics that support machine learning and AI-driven use cases.
Troubleshoot and resolve performance, scalability, and reliability challenges across distributed systems and large-scale data workloads.
Work closely with stakeholders to understand their specific capabilities and needs and be able to think strategically on how to incorporate new capabilities into the system without creating one-off solutions.
Be a team player, continue learning and help teach new or junior members of the team.