Senior Data Engineer

Description

At Liberty Mutual, technology isn't just a part of our business - it's what drives us forward. We deliver our customers peace of mind every day by helping them protect what they value most. Our passion for placing the customer at the center of everything we do is driving a transformational shift at Liberty Mutual.

U.S. based role only

**Qualified employees are encouraged to apply regardless of location**

We are seeking a Senior Data Engineer with strong analytical skills to join our growing Data and Analytics Engineering team in the PL Product/Finance and Vendor Extract space. This is an exciting opportunity for an engineer who brings a blend of technical data engineering expertise and business analyst instincts - someone who not only builds data solutions but deeply understands the data they work with and the business problems they solve.

Operating as an agile team within a Fortune 100 company, you will be on the front edge of an IT transformation, working collaboratively with business stakeholders, data scientists, and engineering peers to design and deliver high-quality data solutions.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines, data models, and integration processes using modern cloud-based technologies (AWS, Snowflake, etc.)
• Analyze complex data sets and technical problems to design effective, flexible, and innovative solutions that meet business needs
• Collaborate closely with Product Owners, business stakeholders, and cross-functional teams to translate business requirements into technical data solutions
• Build and support ETL/ELT pipelines for structured and unstructured data from internal and external sources, including APIs, flat files, and enterprise applications
• Implement data quality checks, validation rules, reconciliations, exception handling, and monitoring to ensure data integrity
• Develop automated tests across multiple scopes (Unit, System, Integration, Regression) to ensure reliability and accuracy of data solutions
• Perform data profiling, root cause analysis, and troubleshooting of data issues across production and non-production environments
• Document data flows, data dictionaries, and technical specifications to support data governance and knowledge sharing
• Identify and recommend continuous improvement opportunities across data engineering processes and tooling
• Collaborate with engineers and architects, and actively contribute to code reviews and engineering best practices
• Leverage AI-assisted development tools like GitHub Copilot

• Actively uses AI tools to improve the speed, quality, and scale of work; can point to concrete workflows where AI adds value; critically evaluates and validates AI-generated output; and applies AI responsibly, protecting confidential and customer data.

Qualifications

• Bachelor’s degree in computer science, Engineering, Information Systems, or a related technical or business discipline (or equivalent experience)
• 5-6 years of experience in data engineering, data analytics, or a related technical role
• Strong proficiency in SQL, including writing complex queries, data modeling, and working with relational and analytical databases
• Familiarity with cloud-based data platforms and tools such as Snowflake, AWS or similar technologies
• Curious and open to exploring some mainframe technologies
• Strong analytical and problem-solving skills with the ability to interpret and communicate data-driven insights
• Demonstrated ability to work in an agile, squad-based environment with cross-functional teams
• Proactive, self-motivated, and a logical thinker with strong attention to detail
• Excellent written and verbal communication skills, with the ability to clearly convey technical concepts to non-technical stakeholders
• Experience with data integration and ETL/ELT tools such as IDMC
• Familiarity with CI/CD pipelines, version control (Git), and software development best practices

Preferred:

• Understanding of Cloud / Hybrid data architecture concepts
• Exposure to data governance, data quality frameworks, or master data management concepts
• Experience in the insurance or financial services industry is a plus

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