Principal Data Engineer - H&B Data & Analytics Platform

Our Health and Benefits business helps large and mid-size clients control health and welfare plan costs, improve health outcomes and promote employee engagement through broad-based, state-of-the-art interventions. We provide solutions encompassing creative plan design, vendor evaluation and management, pricing and funding strategies, data analytics, valuation support, legal compliance and governance strategies. We also provide specialty consulting services including clinical/health management program design, pharmacy solutions, disability/absence management strategies and claims audit services. Product-based solutions such as our pharmacy purchasing coalition round out our broad-based suite of offerings.

About the role

The Principal Data Engineer is a senior, hands‑on technical leader responsible for designing, building, and evolving scalable, reliable, and cost‑efficient data and analytic platforms that power analytics, reporting, and advanced AI/predictive use cases. This role sets engineering standards, drives platform modernization, and mentors engineering teams while partnering closely with architecture, analytics, product, and governance stakeholders.

Technical Leadership & Architecture

• Lead the design and evolution of enterprise‑scale data platforms, including ingestion, transformation, storage, and consumption layers
• Define and enforce data engineering standards, patterns, and best practices (., pipeline design, testing, CI/CD, observability)
• Serve as a technical authority on data architecture decisions, trade‑offs, and platform strategy

Platform & Pipeline Engineering

• Design, build, and optimize high‑performance batch and streaming data pipelines
• Improve reliability, performance, scalability, and cost efficiency of existing data assets
• Implement reusable frameworks and reference architectures to accelerate delivery across teams

Databricks & Lakehouse Focus (Strongly Preferred)

• Design and operate Databricks‑based lakehouse solutions using Delta Lake, Spark, and notebooks/jobs
• Establish best practices for Databricks workspace architecture, security, performance tuning, and cost management
• Lead or support migrations to Databricks and modernization of legacy data pipelines

Data Quality, Governance & Security

• Partner with data governance and security teams to embed data quality checks, metadata, lineage, and access controls into pipelines
• Ensure platforms support regulatory, compliance, and enterprise risk requirements

Cross‑Functional Collaboration

• Work closely with data architects, analytic engineers, data scientists, product owners, and business stakeholders
• Translate business and analytic requirements into scalable technical solutions
• Influence roadmaps by tying platform investments to measurable business outcomes

Mentorship & Engineering Excellence

• Mentor senior and mid‑level engineers; raise the overall technical bar of the team
• Lead by example through hands‑on contribution to critical pipelines and platform components