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About This Role
WHY DO WE NEED YOU?
Verifi, part of Saint-Gobain, is an expanding global SaaS and IoT technology company focused on the construction industry. Dedicated to connecting the physical world with intelligent software. We are revolutionizing the industrial construction space. We provide end-to-end solutions—from embedded sensors to cloud-based analytics—that help companies boost efficiency, ensure sustainability, and unlock actionable insights. We are building a supportive, high-performance team to drive the next wave of digital transformation. Join us to build, scale, and innovate at the edge.
The Senior Data Engineer will be responsible for owning the AI and data pipeline for the engineering organization. The ideal candidate will act as the subject matter expert around Verifi's data. The role will support both development and data science teams to work with our data.
Reporting to the CTO, the Senior Data Engineer is a key part of the engineering organization working across teams to set the standards around secure and scalable data access, including real time streams.
This is a highly visible role to help drive the architecture for Verifi's data backend.
IS THIS JOB FOR YOU?
This is a unique role in Engineering driving industry proven AI and data pipelines while working with existing technologies (e.g., Azure Data Explorer, SQL Server)
- Pipeline Architecture: Architect real-time, transactional, and reporting data pipelines
- Infrastructure Management: Responsible for the data backend design using existing technologies (e.g, Azure Data Explorer, SQL Server)
- Automation Focused: Designed a universal semantic layer to automate recurring reports, eliminating manual Tableau workbook intervention, making data available via REST and GraphQL APIs
- Model Integration and Deployment: Developed and deployed predictive machine learning models directly into the data pipeline, specifically for real-time predictions
- Efficiency and Optimization: Ability to analyze data backend performance and eliminate bottlenecks
- Collaboration: Works across disciplines between development, data science, and product teams
- Communication: Strong verbal and written communication skills, organized and detail oriented
REQUIRED EDUCATION:
Bachelors Degree in Computer Science or related field
REQUIRED WORK EXPERIENCE:
7+ years of data engineering experience with machine learning and model development
7+ years of designing data pipeline for both real-time, transactional, and reporting scenarios
Proficient in Kusto Query Language (KQL) around best practices for performance at scale
Proven ability to lead through influence rather than direct authority
Cross collaboration between disciplines, including development, data science, and product
PREFERRED WORK EXPERIENCE:
Experience with Azure Machine Learning
Knowledge of Python for data analysis and mining
Certain locations require pay information be provided in job postings. Saint-Gobain aims to deliver a comprehensive Total Rewards package to support our employees’ wellbeing and help improve daily life for themselves and their families. We believe in the importance of pay transparency in what we offer potential candidates. Provided is the national pay range for this position which is $111,000 - $177,000 per year. However, the base pay offered may vary depending on geographic region, internal equity, job-related knowledge, skills, and experience among other factors.
In addition to base salary, this position is eligible for an annual bonus. The targeted bonus amount is 23% of base salary based on company and individual performance measures. The Total Target Cash range (base pay and annual bonus) for this position, is $130,134 to $217,710. Bonus payments are part of variable compensation and by nature can vary based on company and individual performance and is not a guarantee.
A LITTLE MORE ABOUT US A little more about us
Verifi is an expanding global SaaS and IoT technology company focused on the construction industry. Dedicated to connecting the physical world with intelligent software. We are revolutionizing the industrial construction space. We provide end-to-end solutions—from embedded sensors to cloud-based analytics—that help companies boost efficiency, ensure sustainability, and unlock actionable insights. We are building a supportive, high-performance, team to drive the next wave of digital transformation. Join us to build, scale, and innovate at the edge.
Verifi is a part of Saint-Gobain that provides one-of-a-kind opportunities for innovative problem solvers. We're one of only two companies in the world that is consistently recognized as both a top Global Employer and a Top 100 Global Innovator. But Saint-Gobain's remarkable story and culture of innovation begins with a team of nearly 200,000 creative, diverse and passionate team members collaborating across the globe. We are committed to our mission to improve lives because, every day, we witness the enormous impact of our efforts on the world around us.
With a legacy dating back more than 360 years, Saint-Gobain offers employees the stability and security of a leading Global 500 corporation while operating like multiple small and agile start-ups, where entrepreneurial spirit, pioneering teamwork, and bold, forward-thinking ideas pave new paths. Saint-Gobain is a global company that designs, manufactures, distributes materials and services for the construction and industrial markets.
LEGAL STATEMENT
Saint-Gobain provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, gender, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, marital status, amnesty, or status as a covered veteran in accordance with applicable federal, state and local laws. Saint-Gobain is an equal opportunity employer of individuals with disabilities and supports the hiring of veterans.
Applicants for roles based in Washington state or remote roles that would be worked from Washington state are encouraged to direct any concerns regarding the state’s Pay Transparency laws to the SGNA HR Compliance team at [email protected].
BENEFITS IN JOINING US
Employees have the flexibility to choose the benefits that best fit their individual needs: Health and Wellbeing – Supporting your wellbeing, to thrive in life and work
- Medical, Prescription Drug, Vision, and Dental Insurance
- Healthcare Savings Account and Flexible Spending Account options
- LiveWell Wellness Program
- Employee Assistance Program (EAP)
- Paid Time Off and Paid Parental Leave
Retirement and Protection – Helping to make the future life you want a reality
- 401(k) with Company Match, Retirement Accumulation Plan (RAP), Cash Balance Pension Plan
- Company-provided Life Insurance, AD&D, Short-Term Disability
- Voluntary employee and dependent life insurance, Long-Term Disability, Critical Illness, and Accident Insurance
Additional Benefits – Helping shape the experience and impact you want
- Group Legal
- Identity Theft Protection
- Auto and Home Insurance
- Pet Insurance and Discounts
- Back-up Child & Elder Care
- PerkSpot Employee Discount Program
Job Reference: USA14783
Salary Context
This $111K-$217K range is above the median for Data Engineer roles in our dataset (median: $145K across 47 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 11,520 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Saint-Gobain, 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 $177,000 based on 195 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($164K) sits 7% below the category median. Disclosed range: $111K to $217K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Saint-Gobain AI Hiring
Saint-Gobain has 4 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer, Research Engineer, AI Product Manager. Positions span Remote, US, Courbevoie, Île-de-France, France, Northborough, MA, US. Compensation range: $139K - $217K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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