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About This Role
Job Details
---------------
Location:
Columbus, OH
Category:
Data Engineering
Employment Type:
Full time
Job Ref:
R2625593\-174
Sr Staff Data Engineer \- GE07DE
We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.
Join our team as a Senior Staff AI Data Engineer and lead the charge in developing cutting\-edge AI solutions and data engineering strategies. Embrace our core values of innovation, collaboration, and excellence as you unlock unparalleled growth opportunities in the dynamic field of AI and data engineering. Shape the future of technology with us! Apply now to be part of our innovative journey and make a significant impact!
This role will have a Hybrid work schedule, with the expectation of working in an office location (Hartford, CT; Chicago, IL; Columbus, OH; and Charlotte, NC) 3 days a week (Tuesday through Thursday).
Primary Job Responsibilities
- Lead the implementation of AI data pipelines integrating structured, semi\-structured, and unstructured data to support AI and agentic solutions, including preprocessing techniques such as extraction, chunking, embedding, and grounding (e.g., RAG, retrieval frameworks)
- Develop AI\-driven data systems that enhance data capabilities while ensuring adherence to industry best practices
- Implement and optimize Retrieval\-Augmented Generation (RAG) architectures and integrate them with enterprise data platforms
- Design, build, and optimize scalable batch and streaming data pipelines with a focus on performance, resiliency, and operational efficiency
- Develop and maintain real\-time data streaming pipelines using technologies such as Snowpipe
- Develop data domains and data products to support reporting, analytics, AI/ML, and data science use cases
- Ensure the reliability, availability, and scalability of data pipelines through monitoring, alerting, and incident management
- Implement reliability engineering best practices, including fault tolerance, redundancy, and disaster recovery
- Drive engineering discipline across data platforms, including observability, data quality, lineage, and governance
- Collaborate with DevOps and infrastructure teams to enable seamless deployment and operation of data systems
- Partner with cross\-functional teams to integrate data and AI solutions into business processes and enterprise systems
- Provide architectural leadership in partnership with Data Architects, including defining technical standards and influencing enterprise\-wide practices
- Develop and integrate graph database solutions to support complex data relationships within AI systems
- Apply GenAI approaches to insurance\-specific data use cases and challenges
- Lead the development of AI\-ready data foundations that support scalable, production\-grade solutions
- Ensure data platforms remain resilient, governed, and cost\-efficient, aligned with enterprise cloud and data strategies
- Mentor junior engineers and contribute to communities of practice, promoting best practices, reusable patterns, and engineering standards
- Stay current with advancements in GenAI and apply relevant technologies and methodologies to platform evolution
Skills
- Strong technical expertise in AI\-driven data solutions leveraging modern cloud platforms
- Deep expertise in core data engineering, including advanced SQL, data modeling, and query performance tuning
- Strong experience in ETL/ELT architecture, orchestration frameworks, and pipeline optimization
- Experience working across teams with strong communication and stakeholder management skills
- Proven ability to mentor and develop AI and data engineering talent
- Knowledge of emerging AI and data engineering design patterns
- Strong planning, organization, and execution capabilities
- Ability to lead in a lean, agile, and fast\-paced environment, leveraging Scaled Agile practices
- Strong analytical and problem\-solving skills with the ability to translate business requirements into technical solutions
- Demonstrated leadership capability to own architecture decisions and drive cross\-team alignment
- Effective collaboration, decision\-making, and relationship\-building skills
- Strong interpersonal skills with the ability to provide thought leadership in a dynamic environment
Qualifications
- Candidates must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I\-983 Training Plan endorsement for this position.
- Bachelor’s degree in Computer Science, Artificial Intelligence, or a related field
- 8\+ years of data engineering experience with deep expertise in SQL, data modeling, and large\-scale data processing systems
- Proven experience designing and optimizing ETL/ELT pipelines and orchestration frameworks in enterprise environments
- Experience supporting Generative AI data engineering use cases
- Hands\-on experience implementing production\-ready, enterprise\-grade GenAI data solutions
- Experience implementing RAG pipelines, including retrieval, chunking, embedding, and grounding techniques
- Experience operationalizing GenAI pipelines in production environments
- Hands\-on experience with cloud ecosystems (AWS, GCP, Azure, Snowflake) and Python\-based data engineering stacks
- Proven ability to deliver resilient, governed, and cost\-efficient data platforms at scale
- Experience with vector databases and graph databases, including design and optimization
- Experience working with unstructured data for GenAI applications
- Experience implementing data governance practices, including data quality, lineage, and data cataloging at scale
- Proficiency in building AI data pipelines that integrate structured and unstructured data with preprocessing techniques
- Strong programming skills in Python
- Strong communication skills and ability to explain technical concepts to a broad set of stakeholders
Preferred Qualifications
- Experience designing multi\-cloud or hybrid AI data solutions
- AI\-related certifications
- Experience in the P\&C insurance industry
- Contributions to open\-source AI projects or research in Generative AI
Compensation
The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford’s total compensation package for employees. Other rewards may include short\-term or annual bonuses, long\-term incentives, and on\-the\-spot recognition. The annualized base pay range for this role is:
$135,040 \- $202,560
Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age
Salary Context
This $135K-$202K range is above the median for Data Engineer roles in our dataset (median: $160K across 37 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 3,823 AI roles we're tracking, Data Engineer positions make up 1% of the market. At The Hartford, 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. This role's midpoint ($168K) sits 19% below the category median. Disclosed range: $135K to $202K.
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.
The Hartford AI Hiring
The Hartford has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer. Positions span Columbus, OH, US, Charlotte, NC, US, Hartford, CT, US. Compensation range: $151K - $234K.
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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