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Senior, specialized cloud-data role with specific tooling reduces applicant density despite metro location.
Core data engineering skills are transferable, though financial-services and governance preferences increase domain sensitivity.
Explicit 8+ years plus many mandatory cloud, governance, and tooling requirements implies rigorous filtering.
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Lead and develop a high-performing team designing and modernizing cloud-native data ingestion, integration, and API services within LPL’s AWS ecosystem.
Architect and implement scalable batch, streaming, and event-driven data pipelines using AWS services such as S3, Glue, Lambda, Kinesis, and Step Functions, with governance and security embedded.
Drive the modernization of legacy data feeds including migration efforts, API standardization, infrastructure automation (IaC), and collaboration across engineering and governance teams.
8+ years of experience in data engineering, software engineering, and/or cloud engineering.
Bachelor’s degree in Data Science, Computer Science or related discipline; Master’s degree preferred.
Hands-on expertise with AWS cloud data lakes (S3, Glue, Lake Formation), data pipeline orchestration (Airflow, Step Functions, dbt), programming in Python and/or SQL, and infrastructure as code (Terraform/CloudFormation).
Experience leading and developing high performing teams.
Experienced data engineering leader with strong architectural instincts and practical AWS cloud expertise in scalable data pipeline modernization.
Skilled in building secure, governed data ecosystems integrating legacy systems to cloud-native platforms with automation and reusable components.
Comfortable working cross-functionally with platform, analytics, AI, governance, and architecture teams to shape technical strategy and enforce best practices.