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About This Role
Date: Sep 25, 2026
Primary Location: Radnor, PA, US
Company: Lincoln Financial
Alternate Locations: Radnor, PA (Pennsylvania); Charlotte, NC (North Carolina); Fort Wayne, IN (Indiana); Greensboro, NC (North Carolina)
Work Arrangement:
Hybrid : Employee will work 3 days a week in a Lincoln office
Relocation assistance: is not available for this opportunity.
Requisition #: 76677
The Role at a Glance
About AIDA Data Strategy and Governance
Lincoln Financial's AI, Data and Analytics (AIDA) organization is building the capabilities required for an AI-enabled business. Within AIDA, Data Strategy & Governance turns priority insurance and retirement use cases into governed, reusable data products and services. We work backward from the decisions and actions AI agents and applications must support, then build the common meaning, quality, metadata, lineage, and secure access needed to trust the result. We deliver with AIDA's infrastructure, platform, engineering, MLOps, and AI-native platform teams, with governance built into the product.
In this role, you will build and operate the data and service components behind Lincoln's priority AI-ready data products. You will use Python and SQL to connect legacy sources, implement transformations and agent-facing interfaces, encode defined business rules, and make testing, lineage, and provenance part of the build. You will learn from senior engineers and business experts while taking increasing ownership of production work.
What you'll be doing
- Work with product owners, data owners, stewards, and business experts to define the decisions the product must support, its authoritative sources, common terms, rules, and ownership.
- Design the product slice, including models, keys, effective-date logic, exceptions, data contracts, API and Model Context Protocol contracts, and versioning.
Build and operate pipelines, dbt models, FastAPI and Pydantic services, and agent-facing tools using SQL, Python, orchestration, and approved AWS services.
- Embed quality, metadata, lineage, provenance, privacy, and security into the product, with tests and evaluations that prove source-to-response correctness and traceability.
- Own deployment and operation through CI/CD, observability, incident response, root-cause fixes, backfills, and recovery procedures.
- Review code, coach Associate engineers, integrate partner contributions, and turn proven solutions into reusable team patterns.
What we’re looking for
Must Haves
- A bachelor's degree or equivalent work experience, plus typically 3 to 5 years of relevant experience or equivalent demonstrated capability.
- Production experience with Python, SQL, FastAPI, Pydantic, asynchronous programming, packaging, and automated testing.
- Hands-on experience developing, deploying, and operating Model Context Protocol servers or comparable agent-facing tool interfaces.
- Experience with AWS data services, orchestration, dbt or comparable transformation tooling, CI/CD, Git-based review, and production troubleshooting.
- Proven ability to turn ambiguous business definitions and expert rules into durable models, contracts, controls, and tested logic, and to own a sensitive production capability with limited supervision.
Nice to Haves
- AWS Lake Formation, Glue, Athena, IAM, Lambda, ECS, API Gateway, Airflow or MWAA, and infrastructure as code.
- Semantic modeling, ontology, graph or vector data, hybrid retrieval, document ingestion, Amazon Bedrock, AgentCore, or agent evaluation. Insurance or financial-services workflows, or experience making contractor, consultant, or other-team deliverables supportable.
Application Deadline
What’s it like to work here?
At Lincoln Financial, we love what we do. We make meaningful contributions each and every day to empower our customers to take charge of their lives. Working alongside dedicated and talented colleagues, we build fulfilling careers and stronger communities through a company that values our unique perspectives, insights and contributions and invests in programs that empower each of us to take charge of our own future.
What’s in it for you:
- Clearly defined career tracks and job levels, along with associated behaviors for each of Lincoln's core values and leadership attributes
- Leadership development and virtual training opportunities
- PTO/parental leave
- Competitive 401K and employee benefits
- Free financial counseling, health coaching and employee assistance program
- Tuition assistance program
- Work arrangements that work for you
- Effective productivity/technology tools and training
The pay range for this position is with anticipated pay for new hires between the minimum and midpoint of the range and could vary above and below the listed range as permitted by applicable law. Pay is based on non-discriminatory factors including but not limited to work experience, education, location, licensure requirements, proficiency and qualifications required for the role. The base pay is just one component of Lincoln’s total rewards package for employees. In addition, the role may be eligible for the Annual Incentive Program, which is discretionary and based on the performance of the company, business unit and individual. Other rewards may include long-term incentives, sales incentives and Lincoln’s standard benefits package.
About The Company
Lincoln Financial (NYSE: LNC) helps people to confidently plan for their version of a successful future. We focus on identifying a clear path to financial security, with products including annuities, life insurance, group protection, and retirement plan services.
With our 120-year track record of expertise and integrity, millions of customers trust our solutions and service to help put their goals in reach.
Lincoln Financial Distributors, a broker-dealer, is the wholesale distribution organization of Lincoln Financial. Lincoln Financial is the marketing name for Lincoln Financial Corporation and its affiliates including The Lincoln National Life Insurance Company, Fort Wayne, IN, and Lincoln Life & Annuity Company of New York, Syracuse, NY. Lincoln Financial affiliates, their distributors, and their respective employees, representatives and/or insurance agents do not provide tax, accounting or legal advice.
Lincoln is committed to creating an inclusive environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.
Follow us on Facebook, X, LinkedIn, Instagram, and YouTube. For the latest company news, visit our newsroom.
Be Aware of Fraudulent Recruiting Activities
If you are interested in a career at Lincoln, we encourage you to review our current openings and apply on our website. Lincoln values the privacy and security of every applicant and urges all applicants to diligently protect their sensitive personal information from scams targeting job seekers. These scams can take many forms including fake employment applications, bogus interviews and falsified offer letters.
Lincoln will not ask applicants to provide their social security numbers, date of birth, bank account information or other sensitive information in job applications. Additionally, our recruiters do not communicate with applicants through free e-mail accounts (Gmail, Yahoo, Hotmail) or conduct interviews utilizing video chat rooms. We will never ask applicants to provide payment during the hiring process or extend an offer without conducting a phone, live video or in-person interview. Please contact Lincoln's fraud team at [email protected] if you encounter a recruiter or see a job opportunity that seems suspicious.
Additional Information
This position may be subject to Lincoln’s Political Contribution Policy. An offer of employment may be contingent upon disclosing to Lincoln the details of certain political contributions. Lincoln may decline to extend an offer or terminate employment for this role if it determines political contributions made could have an adverse impact on Lincoln’s current or future business interests, misrepresentations were made, or for failure to fully disclose applicable political contributions and or fundraising activities.
Any unsolicited resumes or candidate profiles submitted through our web site or to personal e-mail accounts of employees of Lincoln Financial are considered property of Lincoln Financial and are not subject to payment of agency fees.
Lincoln Financial ("Lincoln" or "the Company") is an Equal Opportunity employer and, as such, is committed in policy and practice to recruit, hire, compensate, train and promote, in all job classifications, without regard to race, color, religion, sex, age, national origin or disability. Opportunities throughout Lincoln are available to employees and applicants are evaluated on the basis of job qualifications. If you are a person with a disability that impedes your ability to express your interest for a position through our online application process, or require TTY/TDD assistance, contact us by calling (866) 922-6543.
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 9,653 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Lincoln Financial, 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 $177,825 based on 160 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Lincoln Financial AI Hiring
Lincoln Financial has 8 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, MLOps Engineer. Positions span Radnor, PA, US, Radnor, PA. Compensation range: $171K - $274K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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