Senior Data Engineer

Genworth is a Fortune 500 provider of products and services that help families navigate the financial challenges of aging. They are seeking a Senior Data Engineer to build data pipelines, models, and infrastructure that support analytics and machine learning, collaborating with various teams to enhance data workflows and quality.

Responsibilities:

• Design, build, and maintain scalable ETL/ELT pipelines using Spark, Python, SQL, and Databricks
• Implement reliable ingestion frameworks for batch and streaming data sources
• Ensure pipelines meet SLAs, data quality standards, and production-grade reliability
• Develop robust data models across raw, curated, and semantic layers using Delta Lake
• Create dimensional models, star schemas, and domain-layer datasets for analytics and ML
• Establish and maintain standards for schema design, metadata, and lineage
• Implement data validation, anomaly detection, SLAs, and documentation across pipelines
• Build automated tests, monitoring, and alerting for freshness, completeness, and accuracy
• Partner with platform teams to enhance observability and operational tooling
• Work closely with analysts to understand business KPIs and deliver high-quality curated datasets
• Partner with ML engineers and data scientists to build reusable feature pipelines
• Collaborate with data platform engineers to optimize compute, governance, and orchestration
• Optimize Spark jobs, SQL queries, cluster configurations, and storage patterns for performance and cost
• Improve reliability, reduce technical debt, and simplify complex pipelines
• Apply best practices for RBAC, data privacy, and PII handling using Unity Catalog
• Ensure adherence to compliance frameworks and documentation standards
• Stay current on modern data engineering patterns, Lakehouse architecture, orchestration, and best practices
• Explore new technologies that improve reliability, scalability, and developer productivity

Requirements:

• 7+ years of experience in data engineering or related roles
• Strong expertise with Python, SQL, Spark, and distributed data processing
• Hands-on experience with Databricks, Delta Lake, and Lakehouse architectures
• Deep understanding of ETL/ELT design, data modeling, and data quality practices
• Experience building scalable, production-grade data pipelines
• Experience collaborating with analytics, ML, and product teams
• Strong communication skills with the ability to clarify data requirements and explain technical decisions