Job description

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 organization's 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.

Requirements

Must-haves

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.
Sr Staff Engineer – Data Analytics
The role will work across Databricks, dbt, Tableau, and supporting data technologies to transform raw and curated data into trusted, reusable analytical products.
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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

pythonsqlci/cd

Platform: breezy
Effort: medium