Principal Data Engineer

We are seeking an accomplished Principal Data Engineer to join our Tech Ops organization and play a critical role in designing and scaling our enterprise data platform. This is a senior, hands‑on technical leadership role focused on architecting complex data integration solutions, solving high‑impact technical challenges, and setting standards for data engineering excellence across the organization.

In this role, you will work directly with clients, data partners, and internal engineering teams to design, implement, and evolve large‑scale data pipelines in a modern cloud environment. As a recognized technical authority, you will influence architectural decisions, guide best practices, and support mission‑critical healthcare data workflows across the full data lifecycle.

• Architect Enterprise‑Scale Data Solutions: Design, build, and evolve high‑volume batch and real‑time data pipelines using PySpark, SparkSQL, Databricks Workflows, and distributed processing frameworks.
• Own Platform‑Level Integrations: Develop end‑to‑end ingestion and transformation frameworks integrating Databricks, Snowflake, AWS services (such as S3, SQS, Lambda), and external data provider APIs, with a strong focus on data quality, lineage, and schema evolution.
• Lead Technical Design for Clients: Serve as the technical lead for complex client implementations, defining highly available, fault‑tolerant architectures across multi‑account cloud environments.
• Translate Business Needs into Architecture: Convert complex business and regulatory requirements into scalable technical designs, detailed specifications, and reusable engineering patterns.
• Set Engineering Standards: Establish and champion best practices across CI/CD, code quality, testing, orchestration, monitoring, logging, and observability for data platforms.
• Ensure Security & Compliance: Design and implement security‑first data solutions, including RBAC, encryption, PHI handling, auditability, and alignment with HIPAA and SOC 2 requirements.
• Optimize Performance & Cost: Profile and tune compute workloads, cluster configurations, partitioning strategies, indexing, and caching across Databricks and Snowflake environments.
• Provide Technical Mentorship: Mentor senior and junior engineers, conduct design and code reviews, and raise the overall technical bar across teams.
• Produce Technical Artifacts: Create clear documentation, including architecture diagrams, runbooks, and operational standards that support scalable delivery.

• Deep Data Engineering Experience: 7+ years of hands‑on experience designing and operating large‑scale, distributed data systems in cloud‑based environments.
• Advanced Programming Skills: Expert‑level proficiency in Python, SQL, and PySpark, including performance‑optimized distributed transformations.
• ETL / ELT Architecture Expertise: Proven experience building and operating production‑grade ETL/ELT pipelines using Databricks, Airflow, or similar orchestration frameworks.
• Cloud Platform Expertise: Strong working knowledge of AWS‑based data services (e.g., S3, SQS, Lambda, IAM) or equivalent cloud technologies.
• Data Platform & Streaming Knowledge: Experience working with dbt, Delta Lake, Kafka, or event‑driven architectures in modern data platforms.
• Warehouse & Modeling Experience: Hands‑on experience with Snowflake or other cloud data warehouses, including schema design and performance optimization.
• Distributed Systems Expertise: Demonstrated ability to design scalable, resilient systems requiring specialized knowledge of distributed computing and cloud‑scale data processing.
• Healthcare Data Exposure: Working experience with healthcare data domains such as claims, eligibility, provider, or clinical datasets.
• Technical Communication: Ability to clearly communicate complex technical concepts to both technical and non‑technical partners.
• Education: Bachelors or Masters degree in Computer Science, Engineering, Data Science, or a related field.

• Experience in large‑scale healthcare payer or analytics environments.
• Exposure to machine learning or advanced analytics workflows.
• Familiarity with DevOps or platform engineering practices applied to data systems.
• Experience developing reusable data frameworks or shared platform capabilities.