• Design and build a modern, scalable data platform on Azure • Own the end to end data lifecycle: ingestion, transformation, storage, and serving • Build reliable, observable, and maintainable data pipelines • Work closely with product, analytics, and engineering to turn raw data into trusted datasets • Make architectural decisions that will shape the platform for years to come • Help define best practices around data quality, performance, and cost efficiency

• Lead data conversion projects from inception to completion, ensuring data integrity, accuracy, and accessibility. • Collaborate with the implementation team and customers to gather requirements, understand their client management, portfolio accounting and performance data needs. • Perform data mapping, transformation, and cleansing activities to meet project specifications. • Validate, troubleshoot, and resolve any variances between source data and Ridgeline system generated data during the data conversion process, providing technical support and guidance to customers. • Coordinate closely with project managers and functional consultants to ensure dependencies are identified and accurately accounted for as part of the broader onboarding process. • Partner with Data Science team to improve inefficient processes, enhance system automation, and improve accuracy. • Understand and apply Ridgeline’s value proposition and leverages our established implementation methodology to provide holistic solutions. • Deliver accurate and timely responses to customers and the implementation team (verbal and written) related to data migration tasks. • Contribute to department broader Customer Success initiatives to improve the onboarding experience.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We’re looking for a Senior Software Engineer responsible for designing, building, and maintaining scalable, reliable data pipelines and data platforms that support enterprise analytics and reporting across the organization. This role will partner closely with analytics, data science, and business teams to ensure healthcare data is well-modeled, governed, and readily available for downstream use. The ideal candidate has deep experience with modern data engineering tools, strong data modeling expertise, and a solid understanding of healthcare data. Shift Structure: Remote position, Shift (TBA) Key Responsibilities - Design, build, and maintain scalable data pipelines using Microsoft Fabric and Apache Airflow - Ingest, transform, and integrate data from a variety of sources, including relational systems, APIs, and MongoDB - Implement and manage data solutions aligned to Medallion architecture principles (Bronze, Silver, Gold) - Design and maintain analytical data models, including fact and dimension tables, to support reporting and analytics - Optimize data storage, performance, and reliability across lakehouse and warehouse environments - Ensure data quality, observability, and lineage through validation, monitoring, and documentation - Collaborate with data analysts and BI developers to enable performant, well-modeled datasets for Power BI - Partner with clinical, operational, and technical stakeholders to understand data requirements and constraints - Support data governance, security, and compliance efforts, including HIPAA-related controls - Mentor junior data engineers and contribute to engineering standards and best practices Qualifications - 5+ years of experience as a Data Engineer, Senior Data Engineer, or similar role - Strong experience with Microsoft Fabric (e.g., Lakehouse, Data Warehouse, pipelines, notebooks) - Hands-on experience with Apache Airflow for workflow orchestration and scheduling - Experience working with MongoDB and integrating NoSQL data sources into analytical platforms - Strong SQL skills and experience building performant analytical queries and transformations - Deep understanding of data modeling concepts, including fact and dimension tables - Practical experience implementing Medallion architecture in a data lake or lakehouse environment - Experience working with healthcare data (e.g., EHR/EMR, claims, clinical, revenue cycle, or operational data) - Strong understanding of data engineering best practices around scalability, reliability, and maintainability Preferred Experience - Experience in a healthcare provider, payer, or health technology organization - Familiarity with HIPAA and healthcare data privacy and security requirements - Experience with CI/CD for data pipelines and infrastructure-as-code concepts - Exposure to streaming or near–real-time data processing - Experience supporting enterprise BI platforms such as Power BI Benefits - Competitive salary - 401(k) with employer match - Health, dental, and vision insurance - PTO + paid holidays - Life insurance coverage - Remote flexibility with a national legal scope

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description You will join a high-visibility initiative focused on maturing the organization's data landscape. The Outpost project is moving from an "evolving" data environment to a structured, high-performance ecosystem. As a Data Architect, your mission is to solve current structural inconsistencies and define the long-term data strategy. This is not a "maintenance" role; it is an architectural leadership position meant to ensure data is reliable, accessible, and high-quality for both internal operational efficiency and external customer reporting. By improving schema performance and structure, you will directly enable better business decisions and robust application performance. Key Responsibilities - Define Data Strategy: Architect and implement the long-term data roadmap to transition the organization into a structured, high-performance ecosystem. - Schema Design: Design and optimize complex data models for both transactional (operational) and analytical use cases. - Performance Tuning: Lead database performance tuning and structural improvements to ensure high-speed query execution and system reliability. - Identify Critical Insights: Use analytical intuition to independently identify business-critical data points and define clear analysis goals. - Standardization: Establish and enforce organizational naming conventions, data governance practices, and structural consistency across the landscape. - Automation: Utilize Python to create automation scripts for data exploration, cleaning, and landscape management. - Cross-Functional Collaboration: Partner with Product, Backend Engineering, and Stakeholders to ensure data schemas support both business goals and application UX. Qualifications - Advanced SQL Mastery: Expert-level proficiency in query design, CTEs, window functions, and complex aggregations. - Data Modeling: Proven track record of designing scalable schemas for diverse use cases (Transactional vs. Analytical). - Optimization Expertise: Deep knowledge of database performance tuning and structural improvements. - Functional Python: Proficiency in using Python (loops, functions, dictionaries) for data exploration and automation tasks. - Strategic Intuition: The ability to navigate ambiguity and independently identify how data structure impacts business outcomes. - Self-Direction: Proven ability to manage complex data landscapes and architectural shifts without constant supervision. - Fluent English: Strong communication skills for daily collaboration with international stakeholders. Nice-to-Have Skills - Cloud Warehousing: Specific experience with Snowflake or similar modern cloud data warehouse environments. - Governance: Experience implementing formal data governance frameworks and metadata management. - ETL/ELT Optimization: Hands-on experience optimizing specific data transformation pipelines to reduce latency. - Modern Data Stack: Familiarity with tools like dbt or Airflow for managing the data lifecycle. - LATAM Experience: Experience working in remote, distributed teams across Latin American time zones. Time Zone & Collaboration The role requires collaboration with teams aligned to Central Time (CT), USA. Candidates must overlap at least 4–5 hours daily with US-based stakeholders to ensure seamless architectural alignment.