Interested in this Data Engineer role at Liberty Mutual Insurance?
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Description
NOTE: This role has a hybrid work arrangement (2x per week) in one of the following
Tech hub locations: Plano, TX/Columbus, OH/Boston, MA/Portsmouth,
NH/Indianapolis, IN.
The USRM Data Engineering team is hiring a Director, Data
Engineering, Analytics \& AI to lead and grow our data and analytics
platform capabilities. This role will focus on advancing USRM's data and
analytics foundation through Snowflake's Cortex AI/ML capabilities and the Databricks analytics platform, enabling scalable, intelligent data solutions across the enterprise. The ideal candidate partners closely with data teams and data scientists across USRM to ensure the right data infrastructure, pipelines, and platform capabilities are in place to accelerate model development, advanced analytics, and AI\-driven insights.
This role requires a collaborative leader who partners with
teams across Product, Underwriting, Distribution, Service, Data, and Platform teams to identify and design capabilities which produce seamless, high\-quality data outcomes to enable business agility and customer value using data agents and AI. By working at the intersection of engineering excellence and cross\-functional alignment, the Director will ensure that data infrastructure, pipelines, and platform capabilities are purpose\-built to support the evolving needs of data scientists and domain stakeholders across USRM.
This role combines deep technical leadership, complex program execution, and organizational leadership, and is pivotal to delivering a simplified, future\-ready insurance Data platform.
About The Role
As Director, Data Engineering, Analytics \& AI, you will lead Analytics
platforms and AI data cloud execution while developing a world\-class data
engineering organization.
Engineering Leadership \& Organizational Development
- Foster a culture of technical excellence, innovation, accountability, and continuous learning.
- Oversee talent strategy including hiring, coaching, performance management, and succession planning.
- Establish and scale global engineering teams, including vendor and offshore strategies aligned to capacity and cost objectives.
Technology Strategy \& Delivery
- Shape and drive AI engineering strategy aligned with enterprise architecture, modern engineering practices, and cloud\-native platforms.
- Ensure adoption of best practices across software development, DevOps, security, and quality engineering.
- Balance speed\-to\-market with long\-term scalability, reliability, and operational excellence
- Lead the hands\-on design, build, and deployment of AI\-powered data agents within the Snowflake environment to automate data pipeline orchestration, quality monitoring, and metadata management.
Cross\-Functional Leadership \& Business Alignment
- Partner with Product, Underwriting, Data, Experience, and Architecture teams to deliver business value and seamless customer experiences.
- Collaborate with senior and executive leadership to align and define data engineering capabilities with USRM data \& enterprise data strategy and modernization goals.
- Own delivery outcomes, including timelines, quality, system health, and business impact.
- Represent data teams in planning, budgeting, and resource allocation discussions.
Thought Leadership \& Innovation
- Serve as a thought leader in engineering and modernization, influencing architecture, platform investments, and emerging technologies (e.g., GenAI, cloud data platforms).
- Advance Data engineering practices and contribute to Liberty Mutual’s broader technology community.
Key Responsibilities
- Be accountable for delivery of AI capability milestones across data and analytics platform capability roadmap
- Partner closely with Enterprise Architecture, Data Architecture to ensure that the Data engineering aligns with our go\-forward architecture strategy and aligns with existing architecture blueprints.
- Drive cross\-functional execution across Snowflake, data integrators, platform teams, and downstream systems
- Architect and deliver a shared, interoperable data layer that enables seamless data exchange and unified governance between Snowflake and D
About Us
Pay Philosophy: The typical starting salary range for this role is
determined by a number of factors including skills, experience, education,
certifications and location. The full salary range for this role reflects the
competitive labor market value for all employees in these positions across the
national market and provides an opportunity to progress as employees grow and
develop within the role. Some roles at Liberty Mutual have a corresponding
compensation plan which may include commission and/or bonus earnings at rates
that vary based on multiple factors set forth in the compensation plan for the
role.
At Liberty Mutual, our goal is to create a workplace where everyone feels
valued, supported, and can thrive. We build an environment that welcomes a wide
range of perspectives and experiences, with inclusion embedded in every aspect
of our culture and reflected in everyday interactions. This comes to life
through comprehensive benefits, workplace flexibility, professional development
opportunities, and a host of opportunities provided through our Employee
Resource Groups. Each employee plays a role in creating our inclusive culture,
which supports every individual to do their best work. Together, we cultivate a
community where everyone can make a meaningful impact for our business, our
customers, and the communities we serve.
We value your hard work, integrity and commitment to make things better, and we
put people first by offering you benefits that support your life and
well\-being. To learn more about our benefit offerings please visit: https://www.libertymutualgroup.com/about\-lm/careers/benefits
Liberty Mutual is an equal opportunity employer. We will not tolerate
discrimination on the basis of race, color, national origin, sex, sexual
orientation, gender identity, religion, age, disability, veteran's status,
pregnancy, genetic information or on any basis prohibited by federal, state or
local law.
Qualifications* Bachelor's or Master's Degree in technical or business discipline or related experience; Master's Degree preferred.
- Generally, more than 10 years related experience with 5 years in leadership role.
- In\-depth knowledge of IT concepts, strategies and methodologies and their application to business opportunities. In\-depth knowledge of business operations, objectives and strategies.
- In\-depth knowledge of project planning methodologies and tools and IT standards and guidelines.
- Advanced knowledge of management concepts, practices and techniques. Ability to promote a team environment consisting of several teams.
- Highly developed negotiation, facilitation and consensus building skills.
- Highly developed oral and written communication skills; strong presentation skills.
About UsPay Philosophy: The typical starting salary range for this role is determined by a number of factors including skills, experience, education, certifications and location. The full salary range for this role reflects the competitive labor market value for all employees in these positions across the national market and provides an opportunity to progress as employees grow and develop within the role. Some roles at Liberty Mutual have a corresponding compensation plan which may include commission and/or bonus earnings at rates that vary based on multiple factors set forth in the compensation plan for the role.
At Liberty Mutual, our goal is to create a workplace where everyone feels valued, supported, and can thrive. We build an environment that welcomes a wide range of perspectives and experiences, with inclusion embedded in every aspect of our culture and reflected in everyday interactions. This comes to life through comprehensive benefits, workplace flexibility, professional development opportunities, and a host of opportunities provided through our Employee Resource Groups. Each employee plays a role in creating our inclusive culture, which supports every individual to do their best work. Together, we cultivate a community where everyone can make a meaningful impact for our business, our customers, and the communities we serve.
We value your hard work, integrity and commitment to make things better, and we put people first by offering you benefits that support your life and well\-being. To learn more about our benefit offerings please visit: https://www.libertymutualgroup.com/about\-lm/careers/benefits
Liberty Mutual is an equal opportunity employer. We will not tolerate discrimination on the basis of race, color, national origin, sex, sexual orientation, gender identity, religion, age, disability, veteran's status, pregnancy, genetic information or on any basis prohibited by federal, state or local law.
Fair Chance Notices
- California
- Los Angeles Incorporated
- Los Angeles Unincorporated
- Philadelphia
- San Francisco
Salary Context
This $156K-$311K range is above the 75th percentile for Data Engineer roles in our dataset (median: $150K across 15 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 3,708 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Liberty Mutual Insurance, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills in Demand for This Role
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $178,800 based on 40 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($233K) sits 31% above the category median. Disclosed range: $156K to $311K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Liberty Mutual Insurance AI Hiring
Liberty Mutual Insurance has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer. Positions span Remote, US, New York, NY, US, Indianapolis, IN, US. Compensation range: $225K - $311K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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