Practicebetter is hiring a Staff Data Engineer to serve as a senior technical leader responsible for shaping, scaling, and governing our modern data ecosystem. This role blends architecture, hands-on engineering, platform leadership, and cross-functional partnerships to deliver high-quality data products that power clinical, operational, financial, and analytical outcomes.
What You'll Do
- Design and evolve a scalable, secure, cloud-native lakehouse platform leveraging Databricks, Microsoft Fabric, and dbt.
- Define modeling patterns, governance frameworks, and engineering best practices across the data lifecycle.
- Lead design reviews and guide teams in adopting scalable architectural patterns.
- Drive long-term platform strategy and evaluate emerging technologies.
- Design and implement batch and streaming data pipelines for healthcare data sources (EHR, claims, HL7/FHIR, APIs, flat files, databases).
- Develop modular ingestion, quality, lineage, metadata, and observability frameworks that scale across domains.
- Produce clean, analytics-ready datasets and data models for BI, analytics, and machine learning workloads.
- Implement HIPAA-aligned access patterns and secure handling of PHI.
- Architect Databricks workloads (clusters, jobs, Unity Catalog, Delta Lake) for reliability, performance, and cost efficiency.
- Integrate Databricks and Microsoft Fabric with Azure services and enterprise systems.
- Partner with product managers, data scientists, analysts, clinicians, and business stakeholders to translate healthcare data needs into scalable solutions.
- Lead cross-functional initiatives that modernize and unify the organization’s data ecosystem.
- Mentor senior and mid-level engineers; elevate team capability through technical coaching and standards.
- Drive roadmap planning, platform evolution, and long-term data strategy.
- Champion engineering excellence, reliability practices, documentation quality, and governance.
What We're Looking For
- Bachelor's degree in Computer Science, Engineering, or a related field.
- 7+ years of experience in data engineering.
- 2+ years operating in a senior or staff level engineering role.
- Deep hands-on proficiency with Databricks, Spark, Delta Lake, dbt, and Python.
- Proven ability to design and operate large-scale cloud data platforms (Azure preferred).
- Hands-on experience with Data Engineering, Data Factory, Lakehouse, OneLake.
- Advanced data platform architecture and Lakehouse design expertise.
- Demonstrated ability to design modular, extensible frameworks and guide the long-term evolution of enterprise data platforms.
- Strong command of distributed data processing and cloud native engineering.
- Experience working in HIPAA regulated environments and handling PHI.
- Healthcare data fluency, including regulated data handling and compliance.
- Technical leadership, mentorship, and influence across teams.
- Strong communication skills with both technical and clinical stakeholders.
- Experience with platform reliability, CI/CD for data pipelines, and infrastructure as code.
Nice to Have
- Experience in implementing and supporting Epic integrations, leveraging Cogito Cloud and Caboodle data models, and delivering reliable incremental data pipelines from Caboodle/Clarity.
Technical Stack
- Databricks, Delta Lake, Unity Catalog
- Microsoft Fabric (OneLake, Lakehouse, Data Factory)
- Azure, dbt
- Python, PySpark, Spark SQL
Team & Environment
You will report to the Director of Data Engineering.
Work Mode
This is a remote role open to candidates based in the United States.
We are committed to diversity, equity, and inclusion throughout our recruiting practices.
