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About This Role
Description
### Senior Data Engineer – Enterprise Data Enablement
The Enterprise Data Enablement team is seeking a Senior Data Engineer who can design, develop, and maintain secure, scalable, and efficient data pipelines and platforms. This role will focus on building and deploying data solutions across financial consumer business domains by leveraging existing and new data framework capabilities to acquire, transform, stream, and integrate data. The candidate will also contribute to innovative data engineering solutions, including AI/GenAI/Agentic AI\-ready data capabilities, while collaborating with and supporting a team of data engineers in building scalable, secure, and intelligent data platforms.
### Primary Responsibilities
- Design, build, and maintain reliable, efficient, and scalable data pipelines to acquire, transform, and store large datasets.
- Develop robust data pipelines to collect, process, and compute metrics from various financial data sources while adhering to quality and development standards.
- Contribute to application architecture and technical solutions, and help implement data framework patterns alongside senior engineers and architects.
- Collaborate with cross\-functional teams to deliver optimal data solutions that meet business and platform needs.
- Develop and deploy high\-quality, production\-ready code.
- strong database design principles and data modeling techniques to translate business requirements into scalable data solutions.
- Develop and optimize data models to support analytics, reporting, machine learning, and AI\-driven use cases.
- Support the implementation and enhancement of enterprise data frameworks and contribute to scalable solutions.
- Identify opportunities to improve existing frameworks and help build reusable capabilities across the organization.
- Troubleshoot and resolve data\-related issues in a timely manner.
- Execute unit testing for data pipelines, validate results, and ensure data quality and accuracy; partner with business users for User Acceptance Testing and support deployment activities.
- Follow change management practices and ensure adherence to compliance and regulatory standards.
- Design and build data pipelines and platform capabilities that support AI, Generative AI, and Agentic AI use cases, including model training, inference, retrieval, and orchestration workflows.
- Enable AI\-ready data foundations by developing high\-quality, governed, and reusable datasets for machine learning, large language model (LLM), and intelligent automation solutions.
- Develop and optimize pipelines for structured, semi\-structured, and unstructured data to support GenAI use cases such as semantic search, document intelligence, and retrieval\-augmented generation (RAG).
- Partner with data scientists, ML engineers, architects, and product teams to integrate AI/GenAI capabilities into enterprise data platforms and workflows.
- Implement metadata, lineage, governance, security, and access controls required for responsible AI and enterprise\-scale GenAI adoption.
- Ensure observability, reliability, performance, and data quality for data pipelines, including those supporting AI\-enabled workflows.
### Required Skills / Experience
- 6–8\+ years of experience in data engineering and distributed data processing technologies.
- Hands\-on experience with streaming technologies such as Apache Spark, Beam, or Flink.
- Experience with message brokers such as Apache Kafka.
- Experience working with microservices and batch processing systems.
- Strong programming skills in Java and/or Scala; Python experience preferred.
- Strong SQL development and performance optimization skills.
- Solid knowledge of relational databases (Redshift, PostgreSQL, Snowflake) and NoSQL databases (MongoDB or similar).
- Experience with CI/CD pipelines and version control systems such as Bitbucket and Git.
- Experience with ETL development tools such as Talend or DataStage is a plus.
- Experience with Java Spring Boot; familiarity with React, TypeScript, or Angular is a plus.
- Understanding of cloud\-based data processing, with AWS and/or Azure experience preferred.
- Experience building data pipelines that support analytics, machine learning, and AI workloads.
- Working knowledge of data engineering concepts supporting LLM\-based applications, including retrieval pipelines, embeddings workflows, and unstructured data processing.
- Familiarity with AI/GenAI concepts such as RAG, semantic search, document processing, and model inference workflows.
- Understanding of data governance, security, lineage, and compliance requirements, particularly in regulated environments.
- Exposure to workflow orchestration frameworks and automation patterns is a plus.
- Exposure to vector databases, semantic models, or MLOps/LLMOps concepts is a plus.
- Strong analytical and problem\-solving skills, with the ability to collaborate effectively within technical teams.
### Education, Certifications, and/or Other Professional Credentials
- Bachelor’s degree in Computer Science, Engineering, or a related technology field
### Hours and Work Schedule
- Hours per Week: 40
- Work Schedule: Monday through Friday
*Some job boards have started using jobseeker\-reported data to estimate salary ranges for roles. If you apply and qualify for this role, a recruiter will discuss accurate pay guidance.*
Equal Employment Opportunity
Citizens, its parent, subsidiaries, and related companies (Citizens) provide equal employment and advancement opportunities to all colleagues and applicants for employment without regard to age, ancestry, color, citizenship, physical or mental disability, perceived disability or history or record of a disability, ethnicity, gender, gender identity or expression, genetic information, genetic characteristic, marital or domestic partner status, victim of domestic violence, family status/parenthood, medical condition, military or veteran status, national origin, pregnancy/childbirth/lactation, colleague’s or a dependent’s reproductive health decision making, race, religion, sex, sexual orientation, or any other category protected by federal, state and/or local laws. At Citizens, we are committed to fostering an inclusive culture that enables all colleagues to bring their best selves to work every day and everyone is expected to be treated with respect and professionalism. Employment decisions are based solely on merit, qualifications, performance and capability.
Background Check
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Any offer of employment is conditioned upon the candidate successfully passing a background check, which may include initial credit, motor vehicle record, public record, prior employment verification, and criminal background checks. Results of the background check are individually reviewed based upon legal requirements imposed by our regulators and with consideration of the nature and gravity of the background history and the job offered. Any offer of employment will include further information.
Benefits
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We offer competitive pay, comprehensive medical, dental and vision coverage, retirement benefits, maternity/paternity leave, flexible work arrangements, education reimbursement, wellness programs and more.
Awards We've Received
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Glassdoor Best Place to Work in Consulting, Finance \& Insurance
Human Rights Campaign Corporate Equality Index 100 Award
Newsweek America's Most Charitable Company
The Banker's
US Bank of the Year
Dave Thomas Foundation’s Best Adoption\-Friendly Workplace
Disability:IN Best Places to Work for Disability Inclusion
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 Citizens, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Citizens AI Hiring
Citizens has 1 open AI role right now. They're hiring across Data Engineer. Based in Johnston, RI, US.
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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