Job SummaryThe Principal Data Architect is the senior technical authority accountable for maintaining a pristine data environment and clean ontologies. This role designs, builds, and stewards the enterprise analytics data architecture that the PPL's data runs on. This is a hands-on, builder-operator role. You set the standard, build to it, and give PPL the credibility to make it the default.
Key ResponsibilitiesData Ecosystem
- Design and own the governed data products the analytics function delivers from: certified Gold-layer datasets, a shared semantic and metric layer, and reusable client-ready data models. Every data consumer should work from one definition of every core metric across all PPL initiatives.
- Eliminate rework by building configurable, standards-based patterns that handle regulatory and program variability without separate codebases.
- Set the certification standard for data, including lineage, quality checks, and sign-off, so data outputs are defensible before they leave the building.
- Partner with data leadership to translate recurring client needs into platform capabilities, reducing time from client question to trusted answer.
Cloud Data Architecture & Platform
- Own the target-state architecture for PPL's analytics platform on Databricks (Unity Catalog, medallion Bronze/Silver/Gold, Delta Lake), including reference patterns, standards, and technical roadmaps for storage, integration, metadata, and lifecycle management.
- Design for nationwide scale and regulatory variability across program and state data domains.
Master Data Management & Entity Resolution
- Architect and build PPL's enterprise master data layer and steward its master entity indices, linking participant and provider identities across systems.
- Lead ingestion and integration of third-party datasets that extend PPL's proprietary data in support of its mission.
Data Ingestion, Transformation & Engineering
- Build and optimize batch and streaming pipelines (Spark, Python, SQL, dbt, Azure Data Factory or equivalent), with CI/CD, automated testing, and version control as standard practice.
- Optimize performance, partitioning, query efficiency, and storage and compute cost.
Data Quality, Governance & Compliance
- Work with the VP, Analytics and the Data Governance Committee to define and enforce data quality, lineage, and documentation as requirements of every pipeline and dataset.
- Implement governance, security, access-control, and metadata standards aligned with HIPAA/HITECH, CMS/Medicaid program integrity, SOC 2, and applicable state requirements, in partnership with Compliance.
Standards, Assurance & Technical Leadership
- Serve as the authoritative technical voice on PPL's analytics data estate. Codify architecture standards and provide independent technical assurance on designs before they reach production, clients, or regulators.
- Partner with Engineering, Compliance, Finance, analytics, and business leadership on long-term data strategy and modernization.
- Lead and develop a team of 2 data engineers: assign work, review output, and mentor through demonstrated competence and transparent working practice.
All other duties as assigned.
What Success Looks Like in 12 Months- Analysts deliver from certified datasets with a single metric definition across all states, with no manual cross-state reconciliation.
- A production master person index links participants and providers to Medicaid identifiers with measured accuracy.
- State-specific code paths are consolidated into governed, configurable patterns.
- Every dataset used in a deliverable carries documented lineage and passes automated quality checks.
- Time from new client data request to delivered analysis is reduced.
Required Skills- Deep, demonstrable expertise in cloud data architecture on Databricks and Azure (Unity Catalog, medallion architecture, Delta Lake) at enterprise scale.
- Proven MDM and entity-resolution implementation, including matching strategies, survivorship, and stewardship of person-level data.
- Rigorous data modeling (dimensional and normalized), including semantic and metric layer design for analyst-facing consumption.
- Hands-on engineering credibility with CI/CD, testing, and version control as default practice.
- The communication skill to make rigorous standards land with engineers, consultants, executives, and external clients, and the judgment to hold a defensible line constructively.
Qualifications- Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent professional experience.
- 10+ years in data architecture, data engineering, or enterprise data platform development, including principal-level ownership of enterprise-scale designs.
- Production experience designing and operating Databricks on Azure.
- Hands-on MDM and entity-resolution implementation.
- Track record of establishing architecture standards and governance in multi-stakeholder environments.
Preferred
- Healthcare, Medicaid, or government-program data experience (claims, encounters, eligibility, EVV, PHI frameworks).
- Experience supporting client-facing analytics or consulting teams.
- Familiarity with Apache Iceberg and open table formats.
Working ConditionsRemote, United States only. Occasional travel for [client/team] meetings may be required.
Compensation: $162,500 - $214,000This role is eligible for a base salary within the posted range. Actual compensation will be determined based on a variety of factors, including skills, experience, and geographic location. Compensation may vary for positions based in high cost-of-labor markets.
The above is intended to describe the general contents and requirements of work being performed by people assigned to this classification. It is not intended to be construed as an exhaustive statement of all duties, responsibilities, or skills of personnel so classified 
