Interested in this Data Engineer role at FDM Group?
Apply Now →Skills & Technologies
About This Role
About The Role
------------------
This position requires the successful candidate to work on a W2 directly with FDM. We cannot accept C2C, 1099 or employment sponsorship (e.g. H1\-B) for this position.
FDM is a global business and technology consultancy delivering client and industry driven solutions through our 5 core specialist Practices; Software Engineering, Data \& Analytics, IT Operations, Change \& Transformation, and Risk, Regulation \& Compliance.
FDM is seeking a AI Data Engineer located in Princetonto support a project in the financialsector. Involvement in this project is anticipated to last initially 24 monthsbut may be extended.
This role will be hybrid with requirements to be in office 3 days per week.
About You
-------------
AI Data Engineer (Full Stack / Data Engineering Focus)
Overview:
Seeking an AI Data Engineer to design, build, and scale data and application solutions supporting AI\-driven initiatives. This role will sit at the intersection of data engineering and full stack development, with a focus on enabling advanced analytics, AI use cases, and modern data platforms.
Key Responsibilities:
- Design and develop scalable data pipelines and backend services to support AI/ML initiatives
- Build and enhance full stack applications leveraging modern frameworks and cloud technologies
- Collaborate with business, data, and AI teams to translate requirements into technical solutions
- Integrate data systems and APIs to support analytics and model\-driven use cases
- Optimize data workflows, performance, and system reliability
- Contribute to the development of AI\-enabled products and data platforms
Required Experience:
- 3\+ years of experience in data engineering, software engineering, or full stack development
- Strong programming skills in Java or Python
- Experience building data pipelines, backend services, or full stack applications
- Exposure to AI/ML concepts or working alongside AI\-driven initiatives
- Experience with cloud platforms (AWS, Azure, or GCP)
Preferred:
- Experience with modern frameworks (e.g., React, Node.js)
- Familiarity with big data tools (e.g., Spark, Databricks)
- Understanding of APIs, microservices architecture, and CI/CD pipelines
- Financial services or large enterprise experience
About Us
------------
About FDM
FDM powers the people behind tech and innovation. We spot trends, find top talent, and help businesses stay ahead.
With 35\+ years of experience, we coach, mentor, and launch fresh thinkers from diverse backgrounds into world\-class careers. Partnering with top global companies, we deliver the right talent at the right time—while guiding our people toward exponential growth.
Global impact – 18 centers across North America, APAC, the UK, and Europe
25,000\+ careers launched – and counting
300\+ trusted client partners
Committed to Equal Opportunities
Tech careers should be for everyone. With 75\+ nationalities represented, FDM thrives on diversity, fuels innovation through unique perspectives, and celebrates success together. As an Equal Opportunity Employer and FTSE4Good\-listed company, we ensure every qualified applicant gets a fair shot—no barriers, just opportunities.
Additional Considerations
FDM Group, Inc. is registered to operate and hire employees in select states within the US. We will consider employment applications exclusively from candidates who are either residing in one of the following states or willing to relocate to them: Arizona, California, Colorado, Delaware, Florida, Georgia, Illinois, Indiana, Massachusetts, Maryland, Minnesota, North Carolina, New Jersey, New York, Pennsylvania, Tennessee, Texas, Utah, and Virginia.
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 FDM Group, 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.
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.
FDM Group AI Hiring
FDM Group has 1 open AI role right now. They're hiring across Data Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.