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About This Role
Progressive is dedicated to helping employees move forward and live fully in their careers. Your journey has already begun. Apply today and take the first step to Destination: Progress.
As an intermediate data engineer on the Customer Relationship Management (CRM) Emerging Technology team, you'll play a crucial role in deploying and maintaining innovative solutions using cutting\-edge technologies to address complex business challenges within Customer Experience Services. Your expertise in both AI/ML engineering and data engineering will be essential in developing robust and scalable solutions to deploy and operationalize machine learning. You'll build necessary data pipelines to support data science models, enable efficient access to multiple data sources, support packaging, and deploy new or updated models to production. Additionally, you'll work with large language models to help drive our development of generative and agentic AI applications to solve business problems and research the latest technology advancements in data and AI to share with the organization.
This is a remote position for US based work only.
Monday through Friday, 8:00am\-5:00pm ET or CT
Must have qualifications
- Bachelor's Degree or higher in an Information Technology discipline or related field of study and minimum of one year of work experience designing, programming, and supporting software programs or applications.
- In lieu of degree, minimum of two years related work experience designing, programming, and supporting software programs or applications may be accepted.
Preferred Skills
- Cloud experience with preferably AWS (EC2, S3, Athena, Lambda, etc.) and Openshift
- Experience with Linux, Python, Git, Terraform, SQL, building APIs (Flask), containers (Docker)
- Experience understanding business goals and utilizing AI/ML models to help achieve those goals
- Ability to develop and scale web applications / APIs using open source tools
- Self\-learner with ability to scope and recommend new tools (patterns, cloud services, etc.) as required by projects
Compensation* $97,650 \- $108,500/year
- Gainshare annual cash incentive payment up to 30% of your eligible earnings based on company performance
Benefits* 401(k) with dollar\-for\-dollar company match up to 6%
- Medical, dental \& vision, including free preventative care
- Wellness \& mental health programs
- Health care flexible spending accounts, health savings accounts, \& life insurance
- Paid time off, including volunteer time off
- Paid \& unpaid sick leave where applicable, as well as short \& long\-term disability
- Parental \& family leave; military leave \& pay
- Diverse, inclusive \& welcoming culture with Employee Resource Groups
- Career development \& tuition assistance
- Onsite gym \& healthcare at large locations
Energage recognizes Progressive as a 2025 Top Workplace for: Innovation, Purposes \& Values, Work\-Life Flexibility, Compensation \& Benefits, and Leadership.
Applicants must be authorized to work for any employer in the U.S. without the need or potential need, of current or future sponsorship for employment. Progressive does not hire candidates with (e.g., F\-1 CPT, OPT, or STEM OPT, H\-1B, O\-1, E\-3, TN) statuses for this role.
Equal Opportunity Employer
For ideas about how you might be able to protect yourself from job scams, visit our scam\-awareness page at*https://www.progressive.com/careers/how\-we\-hire/faq/job\-scams/*
\#LI\-Remote
Salary Context
This $97K-$108K range is in the lower quartile for Data Engineer roles in our dataset (median: $160K across 37 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 3,823 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Progressive, 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 Required
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 $208,300 based on 266 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($103K) sits 51% below the category median. Disclosed range: $97K to $108K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Progressive AI Hiring
Progressive has 2 open AI roles right now. They're hiring across Data Engineer. Based in Remote, US. Compensation range: $108K - $164K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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