Reporting to the Director of Data Engineering, the Staff Data Engineer serves 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. Deep experience with Databricks, Python, dbt, and Microsoft Fabric, along with strong fluency in healthcare data and compliance standards, is essential. At its core, you’ll work closely with teams across the organization to deliver governed, high‑quality, analytics‑ready data at scale. 

Our Tech Stack: Databricks, Delta Lake, Unity Catalog, Microsoft Fabric (OneLake, Lakehouse, Data Factory), Azure, dbt, Python, PySpark, Spark SQL. 

What You’ll Do: 

Architecture & Strategy 

• Design and evolve a scalable, secure, cloud‑native lakehouse platform leveraging Databricks, Microsoft Fabric (OneLake, Lakehouse, Data Factory), 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. 

Hands-on Engineering 

• 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. 

 Leadership & Collaboration 

• 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 You’ll Need 

• 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 largescale cloud data platforms (Azure preferred).on experience with 
• 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. 
• 100% remote (ET or CT work hours preferred) 

Preferred 

• Experienced in implementing and supporting Epic integrations, leveraging Cogito Cloud and Caboodle data models, and delivering reliable incremental data pipelines from Caboodle/Clarity. 

As a Machine Learning Engineer, you’re a highly motivated individual with strong fundamentals in computer science and hands‑on experience across the full model development lifecycle—including feature engineering, model development, calibration, deployment, and ongoing monitoring. In this role, you will be flexible, eager to learn new skills, and willing to contribute wherever the team needs support. This Machine Learning Engineer is comfortable working with both traditional tabular machine learning models and modern AI techniques, including prompt engineering and LLM‑based capabilities. 

What You’ll Do 

• Develop and deliver end‑to‑end machine learning solutions, including defining technical requirements, architecting scalable systems, and implementing monitoring, logging, and maintenance workflows. 
• Collaborate closely with engineers, product managers, clinicians, and cross‑functional partners to build new ML products and enhance existing systems. 
• Lead the design and implementation of MLOps frameworks, including pipeline development, CI/CD integration, drift detection, retraining workflows, and rollback strategies. 
• Monitor model performance in production, identify issues, propose remediation steps, and ensure strong test coverage and system reliability. 
• Utilize contemporary software engineering practices to implement scalable, secure, and maintainable AI/ML systems. 
• Develop and customize API integrations to enable seamless connectivity between cloud‑based systems and ML services. 
• Participate in architectural discussions to ensure ML platforms meet compliance, performance, and scalability standards. 

What You’ll Need: 

• Bachelor’s degree in Computer Science, Data Analytics, Software/Computer Engineering, Computational Statistics, Mathematics, or a related discipline. 
• 3+ years of end‑to‑end ML development in production (data prep, feature engineering, modeling, calibration, deployment, monitoring, maintenance). 
• 3+ years of MLOps experience building production pipelines (CI/CD, model registry, feature store), implementing monitoring & drift detection, and automating retraining. 
• 3+ years of Python for production ML (testing, packaging, type hints, linting) and SQL for analytical and production workloads; Scala a plus. 
• 2+ years working with distributed compute and cloud ML environments (e.g., Spark/Databricks on Azure/AWS/GCP) and modern data ecosystems (data lakes, DBMS). 
• Strong debugging and optimization skills across data and ML workflows. 
• Track record of ownership and problem solving—driving measurable impact and quality under ambiguity and evolving requirements. 
• Ability to communicate technical decisions clearly and contribute to documentation and design discussions. 
• Demonstrated system design & architecture skills for scalable, high‑performance ML services and batch/streaming workflows; familiarity with API design and service integration patterns. 
• Proven understanding of tradeoffs in latency, cost, performance, and compliance. 

Preferred 

• 1+ years of Databricks experience + some experience in infrastructure/networking 
• 1+ years implementing LLM‑based solutions in production (prompt/response design, evaluation frameworks, guardrails/safety, latency/cost optimization). 
• 1+ years designing compliant ML platforms (e.g., HIPAA, SOC 2) and working with PHI/PII governance, access controls, and auditability.