Data Engineer – Generative AI, Healthcare Data

Altarum

. Design and operate cloud data platform components, including lakehouse storage, compute, SQL and ELT engines, and reusable transformation workflows using Databricks or comparable platforms .

Posted 9/29/2026full-timeUnited StatesMid-LevelSenior💰 $144,771 - $174,723 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and operating cloud data platforms, with strong capabilities in Python and SQL for building production systems and generative AI applications. Proficient in implementing CI/CD processes, data quality management, and compliance with healthcare data standards.

Highest-signal resume keywords

Python ProgrammingSQL DevelopmentDatabricks PlatformGenerative AI Application DevelopmentCI/CD Implementation

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Hard Skills

Data EngineeringCloud EngineeringAutomated TestingData TransformationInfrastructure as CodeDistributed ProcessingData ModelingData Quality ManagementAPI IntegrationVersion Control

Soft Skills

Technical LeadershipMentoringClear CommunicationProblem SolvingCollaboration

Tools & Technologies

DatabricksSparkMLflowLangChainLlamaIndexDelta LakeDbtGitAWSAzure

Certifications & Qualifications

Databricks Certified Generative AI Engineer Associate

Industry Keywords

Healthcare DataMedicaidFHIRHL7Data GovernanceRegulated ReportingPrivacy by DesignNIST AI Risk Management FrameworkQuality MeasuresDocument Processing

Tech Stack

Tools & technologies

AWSAzureCloudPySparkPythonSparkSQLUnity

About the role

Key responsibilities & impact

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
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
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 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
Define and implement data quality rules, validation, lineage, metadata, and monitoring
Diagnose and resolve production failures, data quality issues, and performance bottlenecks; implement root-cause fixes and maintain operational runbooks
Establish reliability and freshness targets; own monitoring, incident response, and recovery for assigned services
Optimize compute, storage, partitioning, and pipeline execution; track cost and performance and implement guardrails
Own AI application delivery from requirements and solution design through implementation, evaluation, deployment, and production support
Build retrieval-augmented generation and document-intelligence applications, including ingestion, chunking, embeddings, vector search, and retrieval
Use MLflow or comparable tools to manage generative AI evaluation and tracing, evaluation datasets, custom scorers, and prompt/model versioning
Develop LLM applications and agentic workflows using Python, LangChain, LlamaIndex, or comparable frameworks
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
Apply privacy by design, encryption, least-privilege access, secrets management, and secure networking
Implement technical controls and documentation for HIPAA, 42 CFR Part 2, IRB and data-use agreement restrictions, and responsible AI practices informed by the NIST AI Risk Management Framework
Document model behavior, evaluate fairness and explainability, and maintain model cards and governance artifacts
Serve as technical lead for defined projects or workstreams; coordinate contributors, review implementation choices, and resolve blockers
Mentor colleagues through pairing, code reviews, and technical guidance; lead workshops and create tutorials, runbooks, and examples
Explain technical concepts and tradeoffs to varied audiences and contribute technical materials for proposals and client engagements

Requirements

What you’ll need

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
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
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%

Benefits

Comp & perks

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
Flexible work environment
Remote work with occasional in-person collaboration days
Virtual participation in Collaboration Days for non-local employees