Knowledge Graph Engineer Jobs in Arizona (NOW HIRING)

Being good neighbors – helping people, investing in our communities, and making the world a better place – is who we are at State Farm. It is at the core of how we operate and the reason for our success. Come join a #1 team and do some good!

HYBRID Qualified candidates must live within a 180-mile radius of a hub location listed below and should plan to spend time working from home and some time working in the office as part of our hybrid work environment.
HUB LOCATIONS: Bloomington, IL; Richardson, TX; Atlanta, GA; or Tempe, AZ

SPONSORSHIP:  Applicants for this position are required to be eligible to lawfully work in the U.S. immediately; employer will not sponsor applicants for U.S. work authorization (e.g. H-1B visa) for this opportunity.

State Farm is hiring a Software Engineer to design and build end-to-end data pipelines that pull from source systems including Entra ID, ServiceNow, Dynatrace, GitLab, Agility, and more—transforming messy, inconsistent tool data into clean canonical entities using bronze/silver/gold (medallion-style) layers. You’ll tackle problem solving at the core of our platform by delivering deterministic identity resolution (linking the same person/service/deployment across different names, IDs, and schemas), and by building and populating a knowledge graph that connects commits → deployments → incidents → service ownership. You’ll also compute trusted DORA metrics (with confidence scores), define and evolve durable entity models across a 14-domain canonical data model, and build the retrieval surface (APIs, query interfaces, and AI-agent access patterns) so both humans and AI can reliably consume what the platform knows.

This is a green-field implementation of an architecture that’s already been validated end-to-end. While the core architectural approach is established, you’ll have significant ownership in building and evolving the implementation from the ground up. You’ll need strong engineering fundamentals with hands-on experience in event streaming (e.g., Kafka) and lakehouse/data platform tooling (e.g., Databricks), along with a strong data modeling, schema design, and transformation pipeline background. Java and Python are both explicitly needed for the platform’s pipelines and services, and you should be comfortable across the stack (ingestion, transformation, storage, computation, and serving). Bonus experience includes orchestrating event-driven architectures, working with graph/knowledge graph technologies, integrating with CI/CD and observability tools as data sources, and building retrieval/grounding patterns for AI; most importantly, you’ll bring the problem-solving mindset required to handle ghost records, stale data, mismatched org structures, and schema durability through source system migrations (e.g., GitLab→GitHub, Agility→Jira).

What you bring

• Strong software and data engineering background: You’ve designed and shipped end-to-end pipelines that ingest from multiple source systems, handle messy/inconsistent data, and produce results that are correct and dependable (not just fast).
• Hands-on expertise in data modeling and transformations: You’re comfortable defining and evolving canonical entity models using patterns like bronze/silver/gold (medallion architecture), and building transformation pipelines that can survive upstream schema changes and migrations.
• Full-stack data platform experience: You can work across the pipeline end-to-end—ingestion, transformation, storage, computation, and serving—including building reliable interfaces for downstream consumers.
• Proficiency in core engineering languages and architectures: Experience with Java and/or Python (or Scala) plus event-driven architectures and pipeline orchestration tools.
• Knowledge graph / retrieval experience: You’ve built or operated systems involving graph databases or knowledge graph technologies, and you understand how to power query and retrieval surfaces for both humans and AI.
• Proven problem-solving with identity resolution and data quality: You’ve solved challenges where the same entity appears with different names/IDs/schemas across tools, and where data is incomplete, inconsistent, or wrong.
• Clear communication and cross-team collaboration: You work effectively across multiple tracks and stakeholders because this role requires coordination across the organization.
• Experience integrating engineering lifecycle and observability sources: You’ve used Git platforms, CI/CD, incident management, and observability tools as data inputs to pipelines—understanding their data models, edge cases, and failure modes.

Bonus points

• AI/ML integration experience: Building retrieval surfaces, grounding LLMs in structured data, and/or using RAG patterns to reduce hallucinations.
• Lakehouse and streaming experience: Familiarity with lakehouse architectures and/or event streaming (e.g., Kafka) and large-scale data platform engineering.
• Platform mindset: You’ve worked on a system where the data model and metrics definitions mattered more than the UI.

Because work-life balance is a priority at State Farm, compensation is based on our standard 38:45-hour work week!

• Potential starting salary range: $105,000 - $130,000
• Starting salary will be based on skills, background, and experience
• High end of the range limited to applicants with significant relevant experience
• Potential yearly incentive pay up to 15% of base salary

At State Farm, we offer more than just a paycheck. Check out our suite of benefits designed to give you the flexibility you need to take care of you and your family!

• Get Paid! On top of our competitive pay, you are eligible for an annual raise and bonus.
• Stay Well! Focus on you and your family’s health with our robust health and wellbeing programs. State Farm pays most of your healthcare premium, and we offer multiple healthcare plan options, including a high deductible plan. All medical plans provide 100% coverage for in-network preventative care, AND you and your family have access to vision, dental, telemedicine, 24/7 mental health professionals, and much more!
• Develop and Grow! Take advantage of educational benefits like industry leading training programs, top-notch tuition assistance programs, employee resource groups, and mentoring.
• Plan Ahead! Plan for those big moments in life with benefits like fertility/IVF/adoption assistance, college coaching, national discount programs, interactive monthly financial workshops, free financial coaching, and more. You can also start a savings account or consider financing through our State Farm Federal Credit Union!
• Take a Little “You” Time! You will have access to our generous time off policies designed so you can plan around holidays, family events, volunteering, or just to take a relaxing day off. With the opportunity to initially earn up to 20 days annually plus parental leave, paid holidays, celebration day, life leave (40 hours/year), bereavement leave, and community service/education support days, there will be plenty of time for you!
• Give Back! We offer several ways to give back through our Matching Gift Program, Good Neighbor Grant Program, and the Employee Assistance Fund.
• Finish Strong! Plan for retirement using free financial advisors and a 401(k) plan with company contributions of up to 7% of your salary.

Visit our State Farm Careers page for more information on our benefits, locations, and the hiring process of joining the State Farm team!

What you bring

• Strong software and data engineering background: You’ve designed and shipped end-to-end pipelines that ingest from multiple source systems, handle messy/inconsistent data, and produce results that are correct and dependable (not just fast).
• Hands-on expertise in data modeling and transformations: You’re comfortable defining and evolving canonical entity models using patterns like bronze/silver/gold (medallion architecture), and building transformation pipelines that can survive upstream schema changes and migrations.
• Full-stack data platform experience: You can work across the pipeline end-to-end—ingestion, transformation, storage, computation, and serving—including building reliable interfaces for downstream consumers.
• Proficiency in core engineering languages and architectures: Experience with Java and/or Python (or Scala) plus event-driven architectures and pipeline orchestration tools.
• Knowledge graph / retrieval experience: You’ve built or operated systems involving graph databases or knowledge graph technologies, and you understand how to power query and retrieval surfaces for both humans and AI.
• Proven problem-solving with identity resolution and data quality: You’ve solved challenges where the same entity appears with different names/IDs/schemas across tools, and where data is incomplete, inconsistent, or wrong.
• Clear communication and cross-team collaboration: You work effectively across multiple tracks and stakeholders because this role requires coordination across the organization.
• Experience integrating engineering lifecycle and observability sources: You’ve used Git platforms, CI/CD, incident management, and observability tools as data inputs to pipelines—understanding their data models, edge cases, and failure modes.

Bonus points

• AI/ML integration experience: Building retrieval surfaces, grounding LLMs in structured data, and/or using RAG patterns to reduce hallucinations.
• Lakehouse and streaming experience: Familiarity with lakehouse architectures and/or event streaming (e.g., Kafka) and large-scale data platform engineering.
• Platform mindset: You’ve worked on a system where the data model and metrics definitions mattered more than the UI.