Forward Deployed Engineer, Senior - Data & Analytics

Humana•Tampa, FL

1d•$117,600 - $161,700•Remote

About The Position

Humana is seeking a Senior Forward Deployed Engineer to build scalable, secure data and analytics solutions that support analytics, clinical insights, and enterprise decision-making. The role works directly with business and analytics stakeholders to translate real-world problems into working solutions and drive enterprise adoption. It focuses on cloud-based data platforms, Databricks, PostgreSQL, and healthcare data in a regulated environment. The engineer develops data and machine-learning solutions and pipelines, supports Lakehouse architecture, and builds data models and real-time and batch workflows. The role also uses Databricks Genie to enable governed, natural-language analytics and ensures that AI-assisted outputs are accurate, explainable, and compliant with healthcare requirements. As an embedded, customer-facing engineer, this position leads requirements discovery, prototypes and demos solutions, advises business and clinical stakeholders, trains end users, and iterates based on feedback. It also leads technical design discussions, establishes engineering practices, and supports healthcare data security, governance, lineage, auditability, and regulatory readiness.

Requirements

Bachelor's degree in Computer Science, Engineering, or a related field.
At least 5 years of experience building cloud-native data and ML 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, distributed data processing frameworks such as Apache Spark, and the Databricks ML stack, including MLflow, Databricks Notebook, and Runtime ML.
Ability to communicate technical concepts clearly to non-technical business and clinical stakeholders.
Ability to rapidly translate ambiguous, in-the-field requirements into working solutions.

Nice To Haves

Experience in the healthcare or insurance industry; payer experience is strongly preferred.
Familiarity with healthcare standards such as FHIR and HL7.
Experience in a customer-facing, consulting, solutions engineering, or forward-deployed engineering role, engaging directly with end users to gather requirements, run demos, and deliver 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 tools such as Kafka or Event Hubs.
Knowledge of DevOps and CI/CD pipelines, such as Azure DevOps or GitHub Actions.

Responsibilities

Develop scalable, 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 real-time and batch 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 access to data for business stakeholders.
Design and optimize semantic layers and governed datasets for Genie-driven insights.
Collaborate with stakeholders to translate business questions into AI-assisted analytics workflows.
Ensure AI outputs are accurate, explainable, and compliant with healthcare data governance and HIPAA requirements.
Lead design and architecture discussions for enterprise-scale solutions.
Establish and enforce best practices in data and ML engineering, testing, and CI/CD.
Work as an embedded, customer-facing engineer with assigned business or analytical teams to understand workflows, pain points, and success criteria.
Lead end-to-end requirements discovery, including working sessions and workshops, and turn ambiguous business problems into technical specifications and delivery plans.
Prototype, configure, and demonstrate solutions to validate direction and reduce risk.
Advise business and clinical stakeholders, balancing engineering rigor with pragmatic delivery.
Train end users, gather usage feedback, and iterate on delivered solutions to drive adoption.
Partner with product, UX, and business subject-matter experts to identify and deliver solutions.
Ingest, normalize, and optimize healthcare datasets, including producer/agent, broker, commission, and distribution data.
Ensure compliance with HIPAA, HITECH, and enterprise data governance policies.
Implement data security, encryption, masking, and access controls.
Maintain data lineage, auditability, and regulatory reporting readiness.