Sr Staff Engineer – Data Analytics

NationsBenefits is recognized as one of the fastest-growing companies in America and a Healthcare Fintech provider of supplemental benefits flex cards and member engagement solutions. We partner with managed care organizations to provide innovative healthcare solutions that drive growth improve outcomes reduce costs and bring value to their members.

Through our comprehensive suite of innovative supplemental benefits fintech payment platforms and member engagement solutions we help health plans deliver high-quality benefits to their members that address the social determinants of health and improve member health outcomes and satisfaction.

Our compliance-focused infrastructure proprietary technology systems and premier service delivery model allow our health plan partners to deliver high-quality value-based care to millions of members.

We offer a fulfilling work environment that attracts top talent and encourages all associates to contribute to delivering premier service to internal and external customers alike. Our goal is to transform the healthcare industry for the better! We provide career advancement opportunities from within the organization across multiple locations in the US South America and India.

Sr Staff Engineer Data Analytics

Role Overview

We are seeking an experienced Sr Staff Engineer Data Analytics to build and scale the data products models and analytical foundations that power decision-making and external customer reporting across the organization.

The role will work across Databricks dbt Tableau and supporting data technologies to transform raw and curated data into trusted reusable analytical products.

Key Responsibilities

Analytics Engineering & Data Modeling

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.

Semantic Layer & Business Analytics

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.

Data Product Development

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.

Engineering & Automation

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.

Technical Leadership

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 organizations analytics engineering capability.

Qualifications

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.

Ideal Profile

This role is best suited for someone who operates comfortably between analytics software engineering and data architecture. The successful candidate should be 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.