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About This Role
Job description
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Does this position interest you? You should apply – even if you don’t match every single requirement! We're known as an auto glass company. That's the focus of what we do. But beyond the glass, we're so much more. We'll help you build a fulfilling career and encourage you to have a life. Let us be the best place you'll ever work.
A Brief Overview
The AVP, Data Engineering and AI Innovation will provide executive leadership across Safelite's data engineering, data solutions, and AI data agent teams, setting and driving the strategic vision to build, modernize, and continuously evolve the enterprise data platform and centrally managed data assets. This business\-facing technical leader will reimagine what self\-service analytics and enterprise data asset management look like in an AI\-enabled organization, ensuring the business is served with trusted, timely, and governed data and BI assets that confer actionable, data\-driven outcomes. The leader will build and grow high\-performing teams of data engineers, BI developers, and AI/data\-agent practitioners, expanding their skill sets and building cohesiveness across the function while championing innovation, collaboration, and disciplined data quality, security, and governance. The expected result is a scalable, AI\-forward data organization that empowers embedded analysts and business partners.
What you will do
- Set and drive the multi\-year strategic vision and roadmap for enterprise data engineering and data solutions, reimagining self\-service analytics and enterprise data asset management as AI\-enabled capabilities that scale across the organization.
- Provide technical leadership to continually modernize the data platform, finalizing the decommissioning of legacy data integrations and technologies.
- Lead the transition to new data assets and ways of working through effective change management and sustained stakeholder engagement.
- Build, manage, and develop high\-performing teams of data and analytics engineers, growing their skill sets and forging cohesiveness into a robust engine for delivering trusted data.
- Reimagine the role of the data warehouse in embedded\-analyst self\-service, creating governed, intuitive assets and a single source of truth that empower embedded analysts and business users across the enterprise to work responsibly within AI\-native tooling.
- Champion AI innovation, piloting and scaling AI/ML and agentic capabilities (e.g., data agents) that automate data delivery, data quality, and insight generation.
- Own enterprise data asset management and contributions to the engineering\-led semantic layer to enable scalable, governed reporting across the enterprise.
- Partner with business stakeholders across the organization to translate needs into data\-driven solutions while upholding data quality, security, and governance standards.
- Act as a trusted advisor to executive leadership, providing strategic recommendations that drive competitive
- Performs other duties as assigned
- Complies with all policies and standards
Education Qualifications
- Bachelor's Degree in computer science, analytics, engineering, or related field Required
- Master's Degree in computer science, analytics, engineering, business administration, or related field Preferred
Experience Qualifications
- 10\+ years hands\-on experience in data engineering, data science, business intelligence, or analytics Required
- 7\-9 years in a data leadership role, which involved creating and implementing a comprehensive data strategy and building and managing data engineering and/or BI organizations Required
Skills and Abilities
- Ability to set strategic vision and drive multi\-year data and AI roadmaps aligned with business goals and enterprise strategy. (High proficiency)
- Proven ability to reimagine self\-service analytics and enterprise data asset management to leverage emerging technology and best\-in\-class patterns. (High proficiency)
- Ability to provide strategic guidance and build cohesiveness among data engineers, data architects, and data analysts. (High proficiency)
- Deep knowledge and experience with advanced SQL concepts. (High proficiency)
- Hands\-on experience with cloud data warehouses (e.g., Snowflake, Databricks, BigQuery) and relational databases. (High proficiency)
- Deep knowledge of Python. (High proficiency)
- Experience with Tableau, PowerBI, or an equivalent BI visualization tool, and Excel. (High proficiency)
- Excellent collaboration and communication skills to convey complex data insights to non\-technical stakeholders and align initiatives with business goals. (High proficiency)
- Working knowledge of engineering\-led BI semantic layers and script\-based transformation tools (e.g., dbt, AWS Glue, Talend). (High proficiency)
- Experience applying AI/ML and agentic or generative AI approaches to automate data delivery, quality, and insight generation. (Medium proficiency)
- Working knowledge of code management and version control practices (e.g., GitHub) to support collaborative development and reproducibility across teams. (Medium proficiency)
- Experience with large enterprise legacy tooling (e.g., mainframe, Informatica, Oracle Business Intelligence, Cognos). (Medium proficiency)
- Preferred experience with back office ERP systems (e.g., SAP, Oracle Fusion) and an understanding of core supply chain, field, and finance domains. (Medium proficiency)
- Preferred experience with digital logging and clickstream/event data and the platforms that capture it (e.g., Adobe Analytics, Google Analytics/GA4, Tealium, Segment, Snowplow)
- Familiarity with contact center and CX data sources (e.g., telephony/IVR, Genesys, CSAT/NPS/voice\-of\-customer)
This job description in no way states or implies that these are the only duties to be performed by an employee occupying this position. Employees may be required to perform other related duties as assigned to ensure workload coverage. This job description does NOT constitute an employment agreement between the employer and employee and is subject to change by the employer as the organizational needs and requirements of the job change.
This position description is not all inclusive for every aspect of this role. Reasonable accommodations will be made for individuals covered by ADA, ADEA, FMLA and other laws and regulations in accordance with their requirements. Physical and mental demands are not, and should not be construed to be job qualification standards, but are illustrated to help the employer, employee and/or applicant identify tasks where reasonable accommodations may need to be made when an otherwise qualified person is unable to perform the job’s essential duties because of an ADA disability.
Other qualifications may be required to ensure employment eligibility in accordance with local laws, regulations and with Safelite Group, Inc. policies and practices.
This job description in no way states or implies that these are the only duties to be performed by an employee occupying this position. Employees may be required to perform other related duties as assigned to ensure workload coverage. This job description does NOT constitute an employment agreement between the employer and employee and is subject to change by the employer as the organizational needs and requirements of the job change.
This position description is not all inclusive for every aspect of this role. Reasonable accommodations will be made for individuals covered by ADA, ADEA, FMLA and other laws and regulations in accordance with their requirements. Physical and mental demands are not, and should not be construed to be job qualification standards, but are illustrated to help the employer, employee and/or applicant identify tasks where reasonable accommodations may need to be made when an otherwise qualified person is unable to perform the job’s essential duties because of an ADA disability.
Other qualifications may be required to ensure employment eligibility in accordance with local laws, regulations and with Safelite Group, Inc. policies and practices.
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Internal Associates: Already a member of the Safelite team? Apply through your Workday account by searching "Find Open Jobs".
Diversity: Safelite welcomes everyone. We value our diverse workforce and suppliers, and we’re proud to be an equal opportunity employer. Learn more at Careers http://safelite.com/Careers
*Benefit amounts are estimates only. Actual values will depend on benefit elections during enrollment.*
This position description is not all inclusive for every aspect of this role. Reasonable accommodation will be made for individuals covered by ADA, ADEA, FMLA and other laws and regulations in accordance with their requirements. Physical and mental demands are not and should not be construed to be job qualification standards, but are illustrated to help the employer, employee and/or applicant identify tasks where reasonable accommodations may need to be made when an otherwise qualified person is unable to perform the job’s essential duties because of an ADA disability.
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 4,317 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Safelite, 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 $185,000 based on 83 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Safelite AI Hiring
Safelite has 1 open AI role right now. They're hiring across Data Engineer. Based in Columbus, OH, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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