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Lead Data Engineer - Financial Services

GenworthRichmond, Virginia🇺🇸United StatesPosted Oct 13, 2026

Why This Role Stands Out

This hybrid Lead Data Engineer role at Genworth offers a fantastic opportunity to shape scalable cloud data platforms and drive critical financial insights, perfect for experienced engineers who thrive on technical leadership and cross-functional collaboration. You'll have the chance to mentor a team and significantly impact the company's commitment to financial security, so we encourage you to apply!

Quick Overview

Seniority
Mid Senior
Employment type
Full Time
Work mode
Hybrid
Location
Richmond, Virginia, United States
GCPAWSETLSnowflakeAzureKafkaPythonRedshift

Job Description

Genworth is seeking a Lead Data Engineer to design, build, and optimize scalable data platforms that power analytics and decision-making for long-term care, mortgage, and protection products. You will architect cloud-based data solutions, lead the development of robust ETL/ELT pipelines, and ensure data quality, security, and governance across diverse financial data sources. Partnering with data science, actuarial, finance, and product teams, you will translate business needs into reliable datasets, mentor engineers, and drive best practices that support innovation and Genworth's commitment to families' long-term financial security.

Responsibilities

  • Design and architect scalable cloud-based data platforms for financial and insurance use cases.
  • Develop, maintain, and optimize ETL/ELT pipelines integrating internal and external data sources.
  • Implement and enforce data quality, security, and governance standards across datasets.
  • Collaborate with data science, actuarial, finance, and product teams to deliver analytical data models.
  • Lead and mentor data engineers, promoting best practices in coding, testing, and documentation.
  • Support advanced analytics and reporting by ensuring reliable, well-modeled data assets.

Required Skills

  • Python
  • SQLSpark
  • ETL pipeline design
  • Data warehousing (e.g., Snowflake/Redshift/Big
  • Query)
  • Cloud data platforms (AWS/Azure/GCP)
  • Data modeling
  • Data governance and quality
  • Streaming data technologies (e.g., Kafka)
  • CI/CD for data pipelines

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