Interested in this Data Engineer role at Bristol Myers Squibb?
Apply Now →Skills & Technologies
About This Role
Working with Us
Challenging. Meaningful. Life\-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high\-achieving teams. Take your career farther than you thought possible.
Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: careers.bms.com/working\-with\-us .
Position Summary:
Join the Data Discovery Services team within Enterprise Data Platforms, where we deliver and maintain the data foundation platforms that power discovery, search, and data accessibility across the Bristol Myers Squibb enterprise. We run multiple search and discovery services used across the company \- and we're building the next generation of AI\-powered discovery on top of them. Our work makes enterprise data findable, accessible, and actionable for teams across the organization. At the core of this is a semantic knowledge layer \- metadata, taxonomies, and relationships that describe what data means and how it connects \- curated as a data inventory that helps AI work reliably across the enterprise. This is a high\-impact team where engineering, search, and applied AI come together to solve real problems at scale.
As an AI / Data Engineer, you'll be a hands\-on Python developer building the pipelines and integrations that make enterprise data more discoverable. Your primary focus is data engineering \- pipelines, metadata enrichment, transformations, and platform integrations. You'll also contribute to search and AI\-powered retrieval as you grow into the role. Working alongside data engineers, search engineers, and data scientists, this is a hands\-on engineering role \- you'll write code, build pipelines, ship features, and own what you deliver.
Why Join Us?
- Work with a modern stack \- Databricks, Amazon Web Services (AWS), OpenSearch, vector search, semantic knowledge layers, graph databases, and AI agents.
- Build real AI\-powered discovery capabilities, not proofs of concept.
- Grow your skills across data engineering, search, and applied AI on the same team.
- Use AI\-assisted development tools (Claude, Copilot) in your daily workflow.
- Contribute to open\-source projects and shared accelerators.
- Clear path to grow into senior engineering, search specialization, or AI engineering roles.
- Make enterprise data findable and accessible for teams working to improve patient outcomes.
Job Responsibilities:
As an AI / Data Engineer, you'll be a hands\-on Python developer building the pipelines and integrations that make enterprise data more discoverable. Your primary focus is data engineering — pipelines, metadata enrichment, transformations, and platform integrations.
You'll also contribute to search and AI\-powered retrieval as you grow into the role. Working alongside data engineers, search engineers, and data scientists, this is hands\-on engineering role — you'll write code, build pipelines, ship features, and own what you deliver.
- Build and maintain Python pipelines that pull metadata from enterprise data catalogs, enrich it with taxonomy tags and ownership information, and publish it to the discovery platform.
- Tune and optimize search indexes \- adjust analyzers, boost fields, and test queries \- to ensure results match what users need.
- Build a semantic knowledge layer \- chunking documents, generating vector embeddings, and enriching them with semantic knowledge metadata \- to grow a data inventory that supports retrieval\-augmented generation (RAG) and helps AI systems and large language models (LLMs) find and use the right context.
- Maintain integrations that sync ontology and taxonomy changes into the discovery platform, so classifications stay current.
- Investigate and resolve data pipeline issues across Databricks and AWS Glue, trace root causes through metadata enrichment flows, and add data quality checks to prevent recurrence.
- Build API endpoints and Model Context Protocol (MCP) servers that expose search and metadata capabilities to applications and AI agents.
- Design metadata pipelines that map cross\-domain dataset relationships and add them to the cross\-domain join catalog with confidence scores.
- Analyze search patterns, capture user feedback, and improve the discovery experience so the system learns and improves over time.
Qualifications \& Experience:
Required
- Bachelor’s degree in computer science, Data Science, Information Science, Engineering, or a related field. Master's degree preferred.
- Demonstrated proficiency in data engineering, software engineering, or a related technical discipline, with a track record of delivering production data pipelines.
- Proficient Python skills \- this is your primary language day\-to\-day.
- Proficiency in Structured Query Language (SQL).
- Experience with Databricks and AWS Glue for data pipelines and transformations.
- Solid data engineering fundamentals: extract\-transform\-load (ETL/ELT) patterns, data modeling, data quality, and pipeline orchestration.
- Familiarity with AWS cloud services (S3, Lambda, API Gateway, Glue).
- Experience with OpenSearch or Elasticsearch.
- Understanding of metadata management and data cataloging concepts.
- Effective problem\-solving skills and willingness to learn.
- Good communication skills and ability to work collaboratively in a team.
Preferred Qualifications:
- Experience with semantic knowledge layers, RAG patterns, vector search technologies, and building AI\-ready data inventories.
- Familiarity with semantic search, embeddings, chunking strategies, relevance tuning, and semantic knowledge metadata (entity relationships, taxonomies, context enrichment).
- Exposure to AI agent patterns, MCP, or large language model orchestration frameworks.
- Experience with ontology or taxonomy technologies (such as Turtle, Resource Description Framework, Web Ontology Language, or SPARQL query language) or management platforms.
- Familiarity with graph databases or knowledge graph technologies.
- Experience with metadata enrichment, data lineage, or data quality frameworks.
- Exposure to Azure OpenAI, Google Vertex AI, or Amazon Bedrock.
- Experience with Docker, Elastic Container Service (ECS), or CloudFormation.
- Prior exposure to pharma or life sciences.
*If you come across a role that intrigues you but doesn’t perfectly line up with your resume, we encourage you to apply anyway. You could be one step away from work that will transform your life and career.*
Compensation Overview:
Princeton \- NJ \- US: $87,810 \- $106,399 \&\#xa;
The starting compensation range(s) for this role are listed above for a full\-time employee (FTE) basis. Additional incentive cash and stock opportunities (based on eligibility) may be available. The starting pay rate takes into account characteristics of the job, such as required skills, where the job is performed, the employee’s work schedule, job\-related knowledge, and experience. Final, individual compensation will be decided based on demonstrated experience.
Eligibility for specific benefits listed on our careers site may vary based on the job and location. For more on benefits, please visit https://careers.bms.com/life\-at\-bms/.
Benefit offerings are subject to the terms and conditions of the applicable plans in effect at the time and may require enrollment. Our benefits include:
- Health Coverage: Medical, pharmacy, dental, and vision care.
- Wellbeing Support: Programs such as BMS Well\-Being Account, BMS Living Life Better, and Employee Assistance Programs (EAP).
- Financial Well\-being and Protection: 401(k) plan, short\- and long\-term disability, life insurance, accident insurance, supplemental health insurance, business travel protection, personal liability protection, identity theft benefit, legal support, and survivor support.
Work\-life benefits include:
Paid Time Off
- US Exempt Employees: flexible time off (unlimited, with manager approval, 11 paid national holidays (not applicable to employees in Phoenix, AZ, Puerto Rico or Rayzebio employees)
- Phoenix, AZ, Puerto Rico and Rayzebio Exempt, Non\-Exempt, Hourly Employees: 160 hours annual paid vacation for new hires with manager approval, 11 national holidays, and 3 optional holidays
Based on eligibility\*, additional time off for employees may include unlimited paid sick time, up to 2 paid volunteer days per year, summer hours flexibility, leaves of absence for medical, personal, parental, caregiver, bereavement, and military needs and an annual Global Shutdown between Christmas and New Years Day.
All global employees full and part\-time who are actively employed at and paid directly by BMS at the end of the calendar year are eligible to take advantage of the Global Shutdown.
*\*Eligibility Disclosure:* *T* *he summer hours program is for United States (U.S.) office\-based employees due to the unique nature of their work. Summer hours are generally not available for field sales and manufacturing operations and may also be limited for the capability centers. Employees in remote\-by\-design or lab\-based roles may be eligible for summer hours, depending on the nature of their work, and should discuss eligibility with their manager. Employees covered under a collective bargaining agreement should consult that document to determine if they are eligible. Contractors, leased workers and other service providers are not eligible to participate in the program.*
Uniquely Interesting Work, Life\-changing Careers
With a single vision as inspiring as “Transforming patients’ lives through science™ ”, every BMS employee plays an integral role in work that goes far beyond ordinary. Each of us is empowered to apply our individual talents and unique perspectives in a supportive culture, promoting global participation in clinical trials, while our shared values of passion, innovation, urgency, accountability, inclusion and integrity bring out the highest potential of each of our colleagues.
On\-site Protocol
BMS has an occupancy structure that determines where an employee is required to conduct their work. This structure includes site\-essential, site\-by\-design, field\-based and remote\-by\-design jobs. The occupancy type that you are assigned is determined by the nature and responsibilities of your role:
Site\-essential roles require 100% of shifts onsite at your assigned facility. Site\-by\-design roles may be eligible for a hybrid work model with at least 50% onsite at your assigned facility. For these roles, onsite presence is considered an essential job function and is critical to collaboration, innovation, productivity, and a positive Company culture. For field\-based and remote\-by\-design roles the ability to physically travel to visit customers, patients or business partners and to attend meetings on behalf of BMS as directed is an essential job function.
Supporting People with Disabilities
BMS is dedicated to ensuring that people with disabilities can excel through a transparent recruitment process, reasonable workplace accommodations/adjustments and ongoing support in their roles. Applicants can request a reasonable workplace accommodation/adjustment prior to accepting a job offer. If you require reasonable accommodations/adjustments in completing this application, or in any part of the recruitment process, direct your inquiries to [email protected] . Visit careers.bms.com/ eeo \-accessibility to access our complete Equal Employment Opportunity statement.
Candidate Rights
BMS will consider for employment qualified applicants with arrest and conviction records, pursuant to applicable laws in your area.
If you live in or expect to work from Los Angeles County if hired for this position, please visit this page for important additional information: https://careers.bms.com/california\-residents/
Data Protection
We will never request payments, financial information, or social security numbers during our application or recruitment process. Learn more about protecting yourself at https://careers.bms.com/fraud\-protection .
Any data processed in connection with role applications will be treated in accordance with applicable data privacy policies and regulations.
If you believe that the job posting is missing information required by local law or incorrect in any way, please contact BMS at [email protected] . Please provide the Job Title and Requisition number so we can review. Communications related to your application should not be sent to this email and you will not receive a response. Inquiries related to the status of your application should be directed to Chat with Ripley.
R1604590 : AI \& Data Engineer, Data Discovery Services
Salary Context
This $87K-$106K range is in the lower quartile for Data Engineer roles in our dataset (median: $150K across 15 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 3,708 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Bristol Myers Squibb, 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 $178,800 based on 40 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($97K) sits 46% below the category median. Disclosed range: $87K to $106K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Bristol Myers Squibb AI Hiring
Bristol Myers Squibb has 7 open AI roles right now. They're hiring across Data Engineer, AI/ML Engineer, AI Software Engineer. Positions span Princeton, NJ, US, Seattle, WA, US. Compensation range: $106K - $239K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.