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About This Role
Huron helps its clients drive growth, enhance performance and sustain leadership in the markets they serve. We help healthcare organizations build innovation capabilities and accelerate key growth initiatives, enabling organizations to own the future, instead of being disrupted by it. Together, we empower clients to create sustainable growth, optimize internal processes and deliver better consumer outcomes.
Health systems, hospitals and medical clinics are under immense pressure to improve clinical outcomes and reduce the cost of providing patient care. Investing in new partnerships, clinical services and technology is not enough to create meaningful and substantive change. To succeed long\-term, healthcare organizations must empower leaders, clinicians, employees, affiliates and communities to build cultures that foster innovation to achieve the best outcomes for patients.
Joining the Huron team means you’ll help our clients evolve and adapt to the rapidly changing healthcare environment and optimize existing business operations, improve clinical outcomes, create a more consumer\-centric healthcare experience, and drive physician, patient and employee engagement across the enterprise.
Join our team as the expert you are now and create your future.
This role sits within a strategic investment to embed AI into how we operate, serve customers, and make decisions within our healthcare business. We’re building an healthcare\-wide AI data and context platform with a focus on deep domain expertise embedded throughout our architecture. Our goals are:
Turn structured and unstructured information into trusted, reusable “building blocks” (semantic layers, retrieval services, and agent\-ready interfaces) that accelerate product innovation
Deliver transformational speed and leverage —faster time\-to\-insight, higher automation of knowledge work, and a foundation that scales AI safely and reliably as adoption grows.
Unlock new capabilities across our business. Create the foundation that drives deeper domain innovation and allows cross\-domain collaboration to flourish.
This is a hands\-on technical leader who builds core AI/context data capabilities and leads delivery through architecture, implementation, and mentorship. The role owns key parts of the AI context platform—unstructured ingestion, embeddings, retrieval, semantic layers, and governance—while partnering across teams to ship production\-grade AI data products.
This roles does not have direct reports initially. Leadership is through technical ownership and influence.Key responsibilities (hands\-on \+ technical leadership)
Build and own the AI context platform
Design and implement end\-to\-end pipelines: ingestion parsing/chunking enrichment embeddings vector indexing* retrieval/serving.
- Build scalable patterns for incremental refresh, backfills, re\-embeddings, deduplication, and lineage across unstructured sources.
- Improve retrieval quality (query strategies, hybrid search, metadata filtering, reranking hooks) in partnership with AI engineers.
Deliver semantic and governed data products
- Define and implement semantic layers (metrics/entities) that power BI and agent reasoning consistently.
- Establish data contracts and “context contracts” for AI inputs (schemas, metadata requirements, freshness, citation expectations).
- Ensure datasets and indexes are discoverable, documented, and reusable.
Operational excellence
- Own reliability and performance: monitoring, alerting, SLAs/SLOs, runbooks, incident response, postmortems.
- Optimize cost and latency across warehouse/lakehouse and vector infrastructure.
AI safety, governance, and compliance
- Implement security\-by\-design: RBAC/ABAC patterns, PII redaction, retention controls, audit logging, and safe access pathways for agent tools.
- Partner with Security/Legal/Compliance on guardrails for AI access to enterprise knowledge.
Lead through influence
- Drive technical direction and roadmap decomposition with product/AI/application stakeholders.
- Set best practices for testing, CI/CD, and evaluation (retrieval eval sets, regression tests, online telemetry).
- Mentor engineers via pairing, code reviews, and lightweight enablement sessions.
Required qualifications
- 6\-10\+ years in data engineering/platform roles with significant hands\-on delivery.
- Expert SQL and strong Python (or Scala/Java); strong production engineering habits.
- Proven experience designing cloud data pipelines and operating them reliably at scale.
- Experience working with unstructured data processing and search/retrieval concepts.
- Strong communication skills and ability to lead cross\-functionally.
Preferred qualifications
- Hands\-on experience with vector search and embeddings (pgvector/Pinecone/Weaviate/OpenSearch/Elastic) and retrieval patterns (semantic retrieval, hybrid search, reranking).
- Experience supporting LLM applications (RAG, agent tool interfaces, evaluation/observability).
- Knowledge of knowledge graphs/semantic modeling or metrics layers at scale.
- Experience in regulated environments and mature governance programs.
Example Success measures
- Measurable improvement in AI outcomes: higher retrieval precision/recall, better citation coverage, fewer “missing context” failures.
- Reduced latency/cost per retrieval and improved platform reliability (SLO attainment, lower MTTR).
- Broad adoption of semantic definitions and context contracts across teams.
- Accelerated delivery by enabling others via standards, templates, and mentorship.
Behavioral attributes
- Business\-curious and eager to learn: Proactively learns the functional domain (processes, terminology, KPIs, constraints) and can speak credibly with SMEs and business leaders—not just translate requirements, but help shape the right questions and success measures.
- Stakeholder\-first collaborator: Builds strong relationships with stakeholders, SMEs, and consultants; clarifies goals, constraints, and tradeoffs early; communicates progress and risks clearly; and sets realistic expectations around timelines, scope, and quality.
- Consultative problem\-solver: Approaches requests with a “diagnose before prescribe” mindset—asks smart questions, proposes options, and guides teams toward durable solutions rather than one\-off fixes.
- Influence without authority: Leads through expertise and trust—drives alignment, facilitates decisions, and unblocks teams across functions even without direct reports.
- High ownership and follow\-through: Treats reliability, documentation, and operational readiness as part of the work; finishes what they start; and holds a high bar for production quality.
- Clear communicator for mixed audiences: Can go deep with engineers and also explain concepts plainly to non\-technical partners; writes crisp docs, designs, and runbooks.
- Pragmatic builder mindset: Biases toward shipping value in iterations, validating with users, and improving based on feedback—balancing innovation with maintainability and risk.
- Comfortable with ambiguity: Thrives in early\-stage or evolving spaces (AI/data products), adapts quickly, and turns unclear goals into actionable plans.
- Integrity and stewardship: Handles sensitive data responsibly, respects governance, and advocates for secure\-by\-design patterns while enabling the business to move fast.
The estimated base salary for this job is $140,000 \- $190,000 USD. The range represents a good faith estimate of the range that Huron reasonably expects to pay for this job at the time of the job posting. The actual salary paid to an individual will vary based on multiple factors, including but not limited to specific skills or certifications, years of experience, market changes, and required travel. This job is also eligible to participate in Huron’s annual incentive compensation program, which reflects Huron’s pay for performance philosophy. Inclusive of annual incentive compensation opportunity, the total estimated compensation range for this job is $161,000 \- $237,500 USD. The job is also eligible to participate in Huron’s benefit plans which include medical, dental and vision coverage and other wellness programs. The salary range information provided is in accordance with applicable state and local laws regarding salary transparency that are currently in effect and may be implemented in the future.
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Position Level
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ManagerCountry
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United States of America
Salary Context
This $140K-$237K range is above the median for Data Engineer roles in our dataset (median: $168K across 41 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 3,824 AI roles we're tracking, Data Engineer positions make up 2% of the market. At Huron Consulting 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 254 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($188K) sits 9% below the category median. Disclosed range: $140K to $237K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Huron Consulting Group AI Hiring
Huron Consulting Group has 5 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer. Based in Chicago, IL, US. Compensation range: $129K - $237K.
Location Context
AI roles in Chicago pay a median of $202,000 across 283 tracked positions.
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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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