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About This Role
Thank you for your interest in a career at Regions. At Regions, we believe associates deserve more than just a job. We believe in offering performance\-driven individuals a place where they can build a career \-\-\- a place to expect more opportunities. If you are focused on results, dedicated to quality, strength and integrity, and possess the drive to succeed, then we are your employer of choice.
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Job Description:
At Regions, the Data Engineer focuses on the evaluation, design, and execution of data structures, processes, and logic to deliver business value through operational and analytical data assets. The Data Engineer uses advanced data design and technical skills to work with business subject matter experts to create enterprise data assets utilizing state of the art data techniques and tools.
Primary Responsibilities
Partners with Regions Technology partners to Design, Build, and Maintain the data\-based structures and systems in support of Data and Analytics and Data Product use cases
Builds data pipelines to collect and arrange data and manage data storage in Regions’ big data environment
Builds robust, testable programs for moving, transforming, and loading data using big data tools such as Spark
Coordinates design and development with Data Products Partners, Data Scientists, Data Management, Data Modelers, and other Technical partners to construct strategic and tactical data stores
Ensures data is prepared, arranged and ready for each defined business use case
Designs and deploys frameworks and micro services to serve data assets to data consumers
Collaborates and aligns with technical and non\-technical stakeholders to translate customer needs into Data Design requirements, and work to deliver world\-class visualizations, data stories while ensuring data quality and integrity
Provides consultation to all areas of the organization that plan to use data to make decisions
Supports any team members in the development of such information delivery and aid in the automation of data products
Acts as trusted adviser and partner to business leads\- assisting in the identification of business needs \& data opportunities, understanding key drivers of performance, interpreting business case data drivers, turning data into business value, and participating in the guidance of the overall data and analytics strategy
Ensures compliance with risk management programs, rules and regulations, and cybersecurity practices; identifies opportunities for and supports process improvements; applies disciplined change management practices
This position is exempt from timekeeping requirements under the Fair Labor Standards Act and is not eligible for overtime pay.
Requirements
Ph.D. and four (4\) years of experience in a quantitative/analytical/STEM field
Or Master’s degree and six (6\) years of experience in a quantitative/analytical/STEM field or technical related field
Or Bachelor's degree and eight (8\) years of experience in a quantitative/analytical/STEM field or technical related field
Five (5\) years of working programming experience in Python/PySpark, Scala, SQL
Five (5\) years of working experience in Big Data Technology in Hadoop, Hive, Impala, Spark, or Kafka
Preferences
Background in Big Data Engineering and Advanced Data Analytics
Experience developing solutions for the financial services industry
Experience in Agile Software Development
Experience or exposure to cloud technologies and migrations
Prior banking or financial services experience
Skills and Competencies
Ability to interpret and ensure compliance with applicable rules, regulations, and industry guidance
Experience building data solutions at scale
Experience designing and building relational data structures in multiple environments
Experience with DevOps principals, CI/CD, and Software Development Lifecycle
Experience with No\-SQL databases
Experience with large\-scale data Lakehouses at Enterprise scale
Proven record of accomplishment of delivering operational Data solutions including Report and Model Ready Data Assets
Significant experience working with senior executives in the use of data, reporting and visualizations to support strategic and operational decision making
Strong ability to transform and integrate complex data from multiple sources into accessible, understandable, and usable data assets and frameworks
Strong background in synthesizing data and analytics in a large (Fortune 500\), complex, and highly regulated environment
Strong technical background including database and business intelligence skills
Strong communication skills through written and oral presentations
Experience in the following highly desired and preferred:
Designing and deploying enterprise\-grade Generative AI solutions leveraging modern LLMs preferably with Amazon Bedrock
Developing advanced prompt engineering frameworks, incorporating context management, agent orchestration, skills integration, and response evaluation
Implementing AI governance frameworks, including guardrails, compliance controls and enterprise risk management standards
This position is intended to be onsite, now or in the near future . Associates will have regular work hours, including full days in the office three or more days a week. The manager will set the work schedule for this position, including in\-office expectations. Regions will not provide relocation assistance for this position, and relocation would be at your expense. The locations available for this role are Birmingham, AL, Atlanta, GA or Charlotte, NC.
Regions will not sponsor applicants for work visas for this position at this time. Applicants for this position must currently be authorized to work in the United States on a full\-time basis.
Position Type Full time
Compensation Details
Pay ranges are job specific and are provided as a point\-of\-market reference for compensation decisions. Other factors which directly impact pay for individual associates include: experience, skills, knowledge, contribution, job location and, most importantly, performance in the job role. As these factors vary by individuals, pay will also vary among individual associates within the same job.
The target information listed below is based on the Metropolitan Statistical Area Market Range for where the position is located and level of the position.
Job Range Target:
Minimum: $128,571\.85 USD
Median: $161,520\.00 USD
Incentive Pay Plans: Opportunity to participate in the Long Term Incentive Plan.
Benefits Information
Regions offers a benefits package that is flexible, comprehensive and recognizes that "one size does not fit all" for benefits\-eligible associates. Listed below is a synopsis of the benefits offered by Regions for informational purposes, which is not intended to be a complete summary of plan terms and conditions.
Paid Vacation/Sick Time
401K with Company Match
Medical, Dental and Vision Benefits
Disability Benefits
Health Savings Account
Flexible Spending Account
Life Insurance
Parental Leave
Employee Assistance Program
Associate Volunteer Program
Please note, benefits and plans may be changed, amended, or terminated with respect to all or any class of associate at any time. To learn more about Regions’ benefits, please click or copy the link below to your browser.
https://www.regions.com/about\-regions/welcome\-portal/benefits
Location Details Riverchase Operations Center
Location: Hoover, Alabama
Equal Opportunity Employer/including Disabled/Veterans
Job applications at Regions are accepted electronically through our career site for a minimum of five business days from the date of posting. Job postings for higher\-volume positions may remain active for longer than the minimum period due to business need and may be closed at any time thereafter at the discretion of the company.
Salary Context
This $128K-$161K range is below the median 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 Regions 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 $178,800 based on 40 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($145K) sits 19% below the category median. Disclosed range: $128K to $161K.
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.
Regions Financial AI Hiring
Regions Financial has 1 open AI role right now. They're hiring across Data Engineer. Based in Atlanta, GA, US. Compensation range: $161K - $161K.
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
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