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About This Role
At Altarum, we’re driven by a simple but powerful mission: improving the health of individuals with fewer financial resources and populations underserved by the healthcare system. We partner with federal and state agencies to design and deliver solutions that make a real, measurable difference.
About The Role
Altarum is building modern data and AI infrastructure to support public health, Medicaid, analytics, evaluation, and interoperability. As a Data Engineer focused on generative AI and healthcare data, you'll independently design, build, deploy, and operate data pipelines, platform services, and production AI applications — with an emphasis on generative AI systems grounded in healthcare data.
This is a mid-level individual contributor role, typically requiring 3–5 years of relevant experience. You'll own defined projects from requirements and technical design through testing, deployment, and production support. Working with principal engineers and project partners, you'll make implementation decisions within shared architecture and security standards, guide small project teams, and help colleagues build data and AI engineering skills.
What You'll Do
Platform Development and Operations
- Design and operate cloud data platform components, including lakehouse storage, compute, SQL and ELT engines, and reusable transformation workflows using Databricks or comparable platforms
- Develop production Python and SQL code, versioned reusable packages, APIs, and configuration-driven frameworks with automated tests and documentation that help other teams adopt them
- Own CI/CD for data pipelines, AI applications, and supporting services, including environment configuration, automated validation, deployment, release verification, and rollback
- Implement infrastructure as code and secure deployment patterns in AWS, AWS GovCloud, Azure, Azure Government, or other approved environments
Data Pipelines and Interoperability
- Own ingestion, transformation, and normalization pipelines for structured, semi-structured, and unstructured healthcare and public health data, including batch and streaming workloads
- Build reusable connectors, data contracts, and curated datasets using Python, SQL, Spark or PySpark, Delta Lake, dbt, or comparable technologies
- Integrate data such as Medicaid and Medicare claims and encounters, FHIR R4, US Core, HL7 v2, social determinants of health, geospatial data, surveys, and qualitative sources as projects require
- Translate analytics, reporting, evaluation, and data science requirements into tested data models and dependable datasets with documented lineage
Data Quality, Reliability, and Cost Management
- Define and implement data quality rules, validation, lineage, metadata, and monitoring for owned pipelines and services
- Diagnose and resolve production failures, data quality issues, and performance bottlenecks; implement root-cause fixes and maintain operational runbooks
- Establish reliability and freshness targets with stakeholders and own monitoring, incident response, and recovery for assigned services
- Optimize compute, storage, partitioning, and pipeline execution; track cost and performance and implement guardrails
AI Application Engineering
- Independently own AI application delivery from requirements and solution design through implementation, evaluation, deployment, and production support, integrating models, data, retrieval, and external services into maintainable applications
- Build retrieval-augmented generation and document-intelligence applications — including ingestion, chunking, embeddings, vector search, and retrieval — to produce source-grounded outputs from healthcare documents and other unstructured data
- Use MLflow or comparable tools to manage generative AI evaluation and tracing, evaluation datasets, custom scorers, and prompt/model versioning; assess output quality, factual accuracy, latency, and cost before and after deployment
- Develop LLM applications and agentic workflows using Python, LangChain, LlamaIndex, or comparable frameworks, with approved services such as Azure OpenAI or Azure AI Foundry
- Deploy and operate AI applications and model endpoints using Databricks Model Serving or comparable services, with governed data access, monitoring, and CI/CD
- Build reusable AI services and automation for healthcare reporting, document extraction, and knowledge retrieval, partnering with data scientists on predictive models when needed
Responsible AI and Security
- Apply privacy by design, encryption, least-privilege access, secrets management, and secure networking; minimize sensitive information in logs, traces, and evaluation datasets
- Implement technical controls and documentation for applicable HIPAA and 42 CFR Part 2 requirements, IRB and data-use agreement restrictions, and responsible AI practices informed by the NIST AI Risk Management Framework
- Work with data science, security, and program teams to document model behavior, evaluate fairness and explainability, and maintain model cards and other governance artifacts
Technical Leadership and Knowledge Sharing
- Serve as technical lead for defined projects or workstreams: break down work, coordinate contributors, review implementation choices, and help resolve blockers alongside principal engineers and project leads
- Mentor colleagues through pairing, code reviews, and technical guidance; lead workshops and create tutorials, runbooks, and examples that help teams adopt reusable data and AI tools
- Explain technical concepts and tradeoffs to varied audiences, and contribute technical materials for proposals and client engagements
What You Bring
- Bachelor's degree in computer science, data science, information systems, engineering, mathematics, statistics, public health informatics, or a related field, or equivalent practical experience
- Typically 3–5 years of relevant experience in data engineering, AI engineering, software engineering, analytics engineering, cloud engineering, or applied technical research, including independent delivery of production systems
- Strong Python and SQL skills, with experience building maintainable code, data models, automated tests, and reliable ingestion/transformation pipelines
- Hands-on experience with a cloud platform and a modern data platform such as Databricks or an equivalent, including distributed processing with Spark or comparable technology
- Experience using Git, CI/CD, environment-specific configuration, and automated deployment to deliver and support production workloads
- Hands-on experience building and deploying generative AI applications with LangChain, LlamaIndex, or comparable frameworks, including retrieval-augmented generation or document processing, prompt design, API integration, evaluation, and monitoring
- Ability to turn requirements into technical designs, manage defined work, troubleshoot independently, and guide collaborators through clear explanations and constructive feedback
Preferred Qualifications
- Databricks Certified Generative AI Engineer Associate certification strongly preferred; comparable generative AI credentials with relevant practical experience also valued
- Databricks Mosaic AI, Vector Search, Model Serving, MLflow evaluation and tracing, and Unity Catalog; experience delivering governed generative AI applications
- Healthcare and Medicaid data, quality measures, regulated reporting, FHIR or HL7, or accessible document generation
- Databricks workflows, Asset Bundles, Delta Lake, PySpark, dbt, orchestration, or infrastructure as code
- Experience leading small technical teams, mentoring colleagues or students, teaching technical subjects, or supervising applied research
Why Join Our Team
You'll own data and generative AI engineering work that supports public health and Medicaid programs; building reusable applications and infrastructure while helping colleagues develop practical skills through mentoring, shared tools, and technical education.
Where You'll Work
Remote with occasional in-person collaboration days
If you’re near one of our offices (Silver Spring, MD; or Novi, MI), you’ll be Hybrid and will join us in person one day every other month (6 times per year) for a fun, purpose-driven Collaboration Day. These days are filled with creative energy, meaningful connection, and team brainstorming.
Non-local employees will join these days virtually and may be asked to join us occasionally in-person.
Work Eligibility & Requirements
- Candidates must be currently eligible to work in the United States; sponsorship is not available.
- All work must be performed within the continental U.S. for the duration of employment, unless required by contract.
- Ability to work core hours aligned with Eastern Time, unless otherwise approved by your manager.
- Remote employees must maintain a dedicated, ergonomically appropriate workspace free from distractions, with reliable internet and a mobile device that supports efficient work.
- Travel Requirements: <10%
At Altarum, we carefully consider a wide range of compensation factors and rely on market indicators to get it right. Individual compensation packages are based on numerous factors unique to each candidate, including skill set, experience, qualifications, and other job-related reasons. This salary range is not a guarantee of compensation, as the final offer amount will vary based on these factors.
In addition to base salary compensation, Altarum Enterprise employees enjoy the following benefits:
- Competitive Medical, Dental and Optical plans
- Generous Paid Time Off, 8 Company observed holidays plus 3 floating holidays
- Tuition Assistance
- 401K Plan (3% employer contribution plus opportunity for gainsharing)
- Life, AD&D & Disability coverage
- A flexible work environment and more!
Altarum is a nonprofit organization focused on improving the health of individuals with fewer financial resources and populations disenfranchised by the health care system. We work primarily on behalf of federal and state governments to design and implement solutions that achieve measurable results. We combine our expertise in public health and health care delivery with technology development and implementation, practice transformation, training and technical assistance, quality improvement, data analytics, and applied research and evaluation. Our innovative solutions and proven processes lead to better value and health for all.
Altarum is an equal opportunity employer that provides employment and opportunities to all qualified employees and applicants without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, or any other characteristic protected by applicable law.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $144K-$174K range is above the median for Data Engineer roles in our dataset (median: $151K across 28 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 9,653 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Altarum, 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 $177,825 based on 160 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($159K) sits 10% below the category median. Disclosed range: $144K to $174K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Altarum AI Hiring
Altarum has 1 open AI role right now. They're hiring across Data Engineer. Based in Remote, US. Compensation range: $174K - $174K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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