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About This Role
The Opportunity
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QuidelOrtho unites the strengths of Quidel Corporation and Ortho Clinical Diagnostics, creating a world\-leading in vitro diagnostics company with award\-winning expertise in immunoassay and molecular testing, clinical chemistry and transfusion medicine. We are more than 6,000 strong and do business in over 130 countries, providing answers with fast, accurate and consistent testing where and when they are needed most – home to hospital, lab to clinic.
Our culture puts our team members first and prioritizes actions that support happiness, inspiration and engagement. We strive to build meaningful connections with each other as we believe that employee happiness and business success are linked. Join us in our mission to transform the power of diagnostics into a healthier future for all.
The Role
At QuidelOrtho, we’re *advancing the power of diagnostics for a healthier future for all* . Join our mission as our next AI/ML Data Engineer to support our Global Data and Analytics team. The AI/ML Data Engineer will be responsible for designing, building, and optimizing data pipelines and infrastructure using Databricks to support AI and machine learning (ML) initiatives. This role will involve working closely with business stakeholders to identify high\-value AI/ML use cases and translating business requirements into technical solutions. The engineer will work to ensure the successful deployment of AI/ML solutions at scale, leveraging Azure services and Databricks tools.
This position will support a hybrid schedule at our San Diego (Summers Ridge) office.
The Responsibilities
- Work directly with business stakeholders to identify and define AI/ML use cases, translating business needs into technical requirements.
- Design, develop, and optimize scalable data pipelines in Databricks for AI/ML applications, ensuring efficient data ingestion, transformation, and storage.
- Build and manage Apache Spark\-based data processing jobs in Databricks, ensuring performance optimization and resource efficiency.
- Implement ETL/ELT processes and orchestrate workflows using Azure Data Factory, integrating various data sources such as Azure Data Lake, Blob Storage, and Microsoft Fabric.
- Collaborate with Data Engineering teams to meet data infrastructure needs for model training, tuning, and deployment within Databricks and Azure Machine Learning.
- Monitor, troubleshoot, and resolve issues within Databricks workflows, ensuring smooth operation and minimal downtime.
- Implement best practices for data security, governance, and compliance within Databricks and Azure environments.
- Automate data and machine learning workflows using CI/CD pipelines through Azure DevOps.
- Maintain documentation of workflows, processes, and best practices to ensure knowledge sharing across teams.
- Perform other work\-related duties as assigned.
The Individual
Required:
- Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent experience).
- 1\-3 years of experience in data engineering, with a strong focus on Databricks and AI/ML applications.
- Proven experience working directly with business stakeholders to identify and implement AI/ML use cases.
- Expertise in Apache Spark and hands\-on experience with Databricks for building and optimizing data pipelines.
- Strong programming skills in Python and Scala for data engineering and machine learning workflows in Databricks.
- Experience with Azure Data Factory, Azure Data Lake, Azure Blob Storage, and Azure Synapse Analytics.
- Proficiency with Databricks Delta Lake for data reliability and performance optimization.
- Familiarity with MLflow and Databricks Runtime for Machine Learning for model management and deployment.
- Knowledge of Azure DevOps for implementing CI/CD pipelines in Databricks\-based projects.
- Strong understanding of data governance, security practices, and compliance requirements in cloud environments.
- Familiarity with emerging Databricks features such as Delta Live Tables and Unity Catalog.
- Ability to travel up to 5\-10%.
- This position is not currently eligible for visa sponsorship.
Preferred:
- Experience with real\-time data processing using Apache Kafka or Azure Event Hubs.
- Master's degree in Computer Science or related technical fields.
The Key Working Relationships
Internal Partners:
- Regular collaboration with business stakeholders to identify and implement AI/ML solutions that drive business value.
- Close interaction with data engineers and cross\-functional teams to ensure the successful integration of data pipelines and AI/ML models.
- Work alongside IT teams to optimize cloud infrastructure and resource allocation within Databricks and Azure.
External Partners:
- Customers and Vendors.
The Work Environment
No strenuous physical activity, though occasional light lifting of files and related materials is required. 30% of time in meetings, working with team, or talking on the phone, 70% of the time at the desk on computer, doing analytical work. Minimal travel required 5\-10%. Travel includes airplane, automobile travel and overnight hotel.
Physical Demands
Typically, 40% of time in meetings; 60% of time at the desk on computer/doing paperwork/ on phone, doing analytical work. Walking, standing, and sitting for long periods of time are routine to accomplish tasks in this role. Specific vision abilities required by this job include close and distance vision and the ability to adjust focus. Ability to travel on short term notice.
Salary Transparency
The salary range for this position takes into account a wide range of factors including education, experience, knowledge, skills, geography, and abilities of the candidate, in addition to internal equity and alignment with market data. At QuidelOrtho, it is not typical for an individual to be hired at or near the top range for their role and compensation decisions are dependent on the facts and circumstances of each case. The salary range for this position is $70,000 to $80,000 and is bonus eligible. QuidelOrtho offers a comprehensive benefits package including medical, dental, vision, life, and disability insurance, along with a 401(k) plan, employee assistance program, Employee Stock Purchase Plan, paid time off (including sick time), and paid Holidays. All benefits are non\-contractual, and QuidelOrtho may amend, terminate, or enhance the benefits provided, as it deems appropriate.
Equal Opportunity
QuidelOrtho believes in Equal Opportunity for all and is committed to ensuring all individuals, including individuals with disabilities, have an opportunity to apply for those positions that they are interested in and qualify for without regard to race, religion, color, national origin, citizenship, sex, sexual orientation, gender identity, age, veteran status, disability, genetic information, or any other protected characteristic. QuidelOrtho is also committed to providing reasonable accommodations to qualified individuals so that an individual can perform the duties. If you are interested in applying for an employment opportunity and require special assistance or an accommodation to apply due to a disability, please contact us at [email protected] .
Salary Context
This $70K-$80K 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 QuidelOrtho Corporation, 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 ($75K) sits 64% below the category median. Disclosed range: $70K to $80K.
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.
QuidelOrtho Corporation AI Hiring
QuidelOrtho Corporation has 1 open AI role right now. They're hiring across Data Engineer. Based in San Diego, CA, US. Compensation range: $80K - $80K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 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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