Sr Staff Engineer – Data Analytics

NationsBenefits•Plantation, FL

About The Position

NationsBenefits is a rapidly growing company in the Healthcare Fintech sector, providing supplemental benefits, flex cards, and member engagement solutions. They collaborate with managed care organizations to offer innovative healthcare solutions that enhance growth, improve outcomes, reduce costs, and deliver value to members. Their offerings include supplemental benefits, fintech payment platforms, and member engagement solutions designed to help health plans provide high-quality benefits that address social determinants of health, thereby improving member health outcomes and satisfaction. The company boasts a compliance-focused infrastructure, proprietary technology, and a premier service delivery model, enabling health plan partners to deliver value-based care to millions. NationsBenefits fosters a fulfilling work environment that attracts top talent and encourages contributions to premier service for both internal and external customers, with the ultimate goal of transforming the healthcare industry. They offer career advancement opportunities within the organization across various US locations, South America, and India.

Requirements

At least 10 years experience in analytics engineering, data engineering, business intelligence, or related disciplines.
Advanced SQL skills and strong experience designing analytical data models.
Production experience with dbt.
Strong experience with Databricks, including Delta Lake and modern lakehouse patterns - direct Databricks experience required.
Experience developing analytical solutions consumed through Tableau or comparable BI platforms.
Strong understanding of dimensional modeling, Gold-layer architecture, semantic modeling, metrics, and data governance.
Experience building reusable data products rather than primarily developing individual reports or dashboards.
Proficiency with Python and scripting/automation.
Experience with Git, CI/CD, testing, code review, and modern software development practices.
Ability to translate ambiguous business requirements into durable technical solutions.

Nice To Haves

Operates comfortably between analytics, software engineering, and data architecture.
Equally comfortable designing an enterprise semantic model, writing dbt transformations, debugging Python, optimizing Databricks workloads, and working with business stakeholders to define what a metric actually means.

Responsibilities

Design, build, test, and maintain production-grade analytical data models using dbt and Databricks.
Develop scalable Gold-layer models that translate operational data into business-ready analytical datasets.
Design dimensional, domain-oriented, and reusable data models supporting reporting, analytics, and downstream data products.
Establish clear separation between raw/curated data, Gold analytical models, and the enterprise semantic layer.
Implement testing, documentation, lineage, version control, and deployment practices for analytical data assets.
Design and evolve semantic models that provide consistent definitions for business metrics, dimensions, and KPIs.
Build analytical foundations optimized for consumption through Tableau and other BI or self-service tools.
Reduce duplicated business logic across dashboards, reports, and analyst workflows by centralizing reusable definitions.
Partner with analysts and business teams to translate business concepts into governed analytical models ready for consumption by business users.
Treat analytical datasets, semantic models, and reusable metrics as data products with defined consumers, ownership, documentation, quality expectations, and lifecycle management.
Build reusable data products that support multiple downstream consumers rather than one-off reporting solutions.
Partner with product, analytics, engineering, and business teams to identify high-value data products and prioritize development.
Improve usability, discoverability, and self-service usage of analytical data across the organization.
Develop scripts and utilities using Python, SQL, shell scripting, or similar technologies to automate data preparation, validation, deployment, monitoring, and operational workflows.
Build repeatable engineering patterns that replace manual analytics processes.
Participate directly in code reviews, debugging, performance optimization, and production support.
Apply software engineering practices including Git, CI/CD, automated testing, modular development, and infrastructure-aware deployment.
Establish standards and design patterns for analytics engineering, modeling, semantic layers, and data products.
Provide technical guidance and mentorship to analytics engineers, analysts, and adjacent data teams.
Review architectures and code while remaining an active contributor to the platform.
Partner with data platform and data engineering teams on architecture, performance, governance, and data quality.
Help define the roadmap for the organization's analytics engineering capability.