Forward Deployed Engineer, Senior - Data & Analytics

Humana•Louisville, KY

1d•Remote

About The Position

Humana is seeking a Senior Forward Deployed Engineer to build scalable, secure data and analytics solutions supporting analytics, clinical insights, and enterprise decision-making. The role works directly with business and analytics stakeholders to translate real-world problems into practical solutions and encourage adoption. It involves building and maintaining data-processing architectures that convert structured and unstructured data into insights at enterprise scale, including in a regulated healthcare environment. The engineer develops data and machine-learning solutions using Databricks, Spark, and cloud-native services; builds batch and real-time pipelines; and supports enterprise data warehouses and Lakehouse architectures. The role also enables AI-assisted analytics with Databricks Genie by developing governed semantic layers and datasets, and by ensuring outputs are accurate, explainable, and compliant with healthcare requirements. As an embedded, customer-facing technical partner, the engineer leads discovery workshops, turns ambiguous requirements into technical plans, prototypes and demonstrates solutions, advises business and clinical stakeholders, and supports adoption through user training and iterative improvements. The role also provides technical leadership on architecture and engineering practices and ensures healthcare data is handled securely, with appropriate governance, lineage, and auditability. This is a remote position with occasional travel to Humana offices for training or meetings. Employees working from home must have internet service with at least 25 Mbps download and 10 Mbps upload speeds and a dedicated workspace that protects member PHI/HIPAA information. The position is scheduled for 40 hours per week. The stated annual pay range is $117,600–$161,700, and the role is eligible for a bonus incentive plan.

Requirements

Bachelor's degree in Computer Science, Engineering, or a related field.
At least 5 years of experience building cloud-native data and machine-learning engineering solutions.
At least 5 years of programming experience in Python (PySpark) and SQL.
At least 3 years of experience deploying data and feature pipelines, model training, and inferencing within the Databricks stack.
At least 3 years of hands-on experience with Databricks, PostgreSQL, and distributed data processing frameworks such as Apache Spark.
At least 3 years of hands-on experience with the Databricks ML stack, including MLflow, Databricks Notebook, and Runtime ML.
Ability to communicate technical concepts clearly to non-technical business and clinical stakeholders and rapidly translate ambiguous, in-the-field requirements into working solutions.
For home-based work, self-provided internet service must provide at least 25 Mbps download and 10 Mbps upload speed.
For home-based work, maintain a dedicated workspace without ongoing interruptions to protect member PHI/HIPAA information.

Nice To Haves

Experience in the healthcare or insurance industry; payer experience is strongly preferred.
Familiarity with healthcare standards such as FHIR and HL7.
Prior customer-facing, consulting, solutions engineering, or forward-deployed engineering experience, including gathering requirements, running demonstrations, and delivering solutions on tight timelines.
Experience with Delta Lake or Lakehouse architecture.
Experience with orchestration tools such as Airflow or Azure Data Factory.
Experience with streaming technologies such as Kafka or Event Hubs.
Knowledge of DevOps and CI/CD pipelines, such as Azure DevOps or GitHub Actions.

Responsibilities

Develop scalable and cost-efficient data and machine-learning solutions using Databricks, Spark, and cloud-native services.
Build and optimize ETL/ELT workflows for structured and unstructured healthcare data, including claims, clinical, provider, and consumer data.
Develop and maintain data models in enterprise data warehouses and support Lakehouse architecture using Databricks, Delta Lake, and cloud storage.
Build batch and real-time data pipelines for analytics and operational use cases.
Develop data transformations using PySpark and SQL within Databricks.
Integrate data from APIs, third-party vendors, and internal systems.
Use Databricks Genie to enable natural-language querying and broader data access for business stakeholders.
Design and optimize semantic layers and governed datasets for trusted Genie-driven insights.
Collaborate with stakeholders to turn business questions into AI-assisted analytics workflows and ensure AI outputs are accurate, explainable, and compliant.
Lead enterprise-scale design and architecture discussions and establish data and ML engineering, testing, and CI/CD best practices.
Work directly with assigned business and analytics teams to understand capabilities, workflows, pain points, and success criteria.
Lead requirements discovery, workshops, and working sessions; translate evolving business needs into technical specifications and delivery plans.
Rapidly prototype, configure, and demonstrate solutions to validate direction and reduce risk.
Advise business and clinical stakeholders while balancing engineering rigor with pragmatic delivery.
Train end users, gather usage feedback, and iterate on solutions to drive adoption.
Partner with product, UX, and business subject-matter experts to identify and deliver solutions.
Ingest, normalize, and optimize healthcare producer/agent, broker, commission, and distribution data for analytics and reporting.
Ensure compliance with HIPAA, HITECH, and enterprise data governance policies.
Implement data security, encryption, masking, and access controls.
Maintain data lineage, auditability, and readiness for regulatory reporting.