Senior Data Analytics Engineering

The Senior Data Analytics Engineer is a critical member of the Data Modernization & Integration organization. This exceptional developer will be responsible for leading a high performing team responsible for designing, building, and modernizing the ingestion, integration, and API services that power LPLs cloud data ecosystem.

This role drives the transformation of legacy data feeds into scalable, governed, cloud-native pipelines and helps define the engineering standards that support LPL’s federated data product operating model. The ideal candidate combines deep hands-on engineering expertise with strong architectural instincts, a passion for automation, and the ability to collaborate across platform, analytics, and AI teams.

Responsibilities:

Modernization & Cloud Engineering

• Architect and lead migration of legacy SQL/SSIS/ETL pipelines into AWS-native ingestion and integration patterns.
• Design and implement scalable batch, streaming, and event-driven pipelines using services such as S3, Glue, Lambda, Kinesis, DynamoDB, and Step Functions.
• Build resilient data movement frameworks with embedded governance (metadata, lineage, security, quality).
• Contribute to decommissioning efforts by rationalizing and replacing legacy pipeline assets.

Integration & API Engineering

• Develop secure, performant APIs using modern tooling (API Gateway, Lambda, GraphQL, REST).
• Standardize integration patterns for reusable ingestion modules and domain onboarding.
• Partner with Enterprise Architecture to align on API standards, patterns, and best practices.

Automation & Platform Enablement

• Implement infrastructure-as-code using tools like Terraform or CloudFormation.
• Develop CI/CD pipelines promoting automation, repeatability, and quality.
• Contribute to shared libraries, frameworks, and templates that accelerate onboarding of new data sources.
• Drive observability improvements through logging, metrics, tracing, and automated alerting.

Cross-Team Collaboration

• Establish, develop and lead a top performing team of data engineers.
• Collaborate with Lakehouse Engineering, Warehouse Engineering, AI Engineering, and Data Product teams to ensure reliable and timely data availability.
• Work closely with governance and security teams to enforce enterprise data standards.
• Actively develop and drive a culture of engineering excellence, setting the tone through example.

Strategic Influence

• Shape our team’s technical roadmap and modernization approach.
• Contribute to architectural discussions and design reviews.
• Advocate for scalable, maintainable, cloud-native engineering practices across the organization.

What are we looking for?

We’re looking for strong collaborators who deliver exceptional client experiences and thrive in fast-paced, team-oriented environments. Our ideal candidates pursue greatness, act with integrity, and are driven to help our clients succeed. We value those who embrace creativity, continuous improvement, and contribute to a culture where we win together and create and share joy in our work.

Requirements:

• Proven track record of leading and developing high performing, engaged teams.
• 8+ years of experience in data engineering, software engineering, and/or cloud engineering.
• Bachelor’s degree in Data Science, Computer science or related field; Master’s degree preferred.
• Demonstrable hands-on experience with:
• Cloud data lake architectures: AWS S3, Glue, Lake Formation, Snowflake, or similar.
• Data lake design patterns: raw, curated, consumption zones; medallion architecture.
• Data versioning and schema evolution: e.g., Delta Lake, Apache Iceberg.
• Data governance and cataloging: including any of the following (preferred experience in multiple tools) Unity Catalog, Collibra, Atlan, AWS Glue Data Catalog.
• Programming: Python and/or SQL (production code, reusable libraries, tests).
• Pipeline orchestration: Airflow, Step Functions, dbt, or similar.
• DevOps for data: Terraform/CloudFormation, CI/CD, monitoring, and runbook creation.
• Strong understanding of data modeling, data quality, and secure data onboarding/governance.
• Experience with both batch and real-time data processing.

Core Competencies:

• Systems Thinking — understands interconnected data flows across platforms.
• Builder Mindset — emphasizes automation, reuse, and simplicity.
• Collaboration — works seamlessly across engineering, architecture, analytics, and operations.
• Leadership — mentors others and elevates the overall engineering discipline.
• Adaptability — thrives in modernization efforts and evolving technology ecosystems

Preferences:

• Bachelor’s degree in Data Science, Computer science or related field; Master’s degree preferred.
• Experience modernizing legacy data feeds and migrating large-scale ingestion workloads to the cloud.
• Knowledge of API management, GraphQL, and federated access patterns.
• Exposure to data mesh concepts or federated data product architectures.
• Background working in regulated industries (financial services strongly preferred).
• Familiarity with observability tools (Dynatrace, CloudWatch, Datadog, OpenTelemetry).
• Experience designing frameworks or reusable integration components.