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.

We are seeking a Lead Data Engineer to architect, build, and scale our next-generation healthcare data platform. In this role, you will lead the effort to design robust pipelines, modernize data architecture, and ensure high-quality ingestion and transformation of clinical and operational data. You’ll collaborate closely with product, analytics, clinical informatics, machine learning, and engineering teams to deliver trusted, timely, and compliant insights. This is a hands-on leadership role ideal for someone who enjoys setting technical direction while still contributing code and guiding stakeholders through complex healthcare data challenges. Responsibilities Architecture & Strategy - Lead design and evolution of our cloud-native data platform built primarily on Google Cloud Platform, including BigQuery, Cloud Storage, Pub/Sub, Cloud Run, Airflow (Cloud Composer), and Healthcare API. - Inform strategic decisions around multi-cloud or AWS interoperability when needed. - Establish data engineering best practices, coding standards, and architectural patterns. Pipeline Development - Build scalable ETL/ELT pipelines using dbt for transformations and Airflow for orchestration. - Develop ingestion pipelines for clinical and administrative data in HL7, FHIR, DICOM, and custom formats. - Develop ingestion and transformation pipelines to be used for AI/ML development and model training. - Implement streaming and batch dataflows using Pub/Sub, Dataflow, and serverless compute. - Support or guide integrations with AWS-based partner systems or AWS-hosted data sources when applicable. Data Modeling & Warehousing - Design and maintain BigQuery datasets, semantic layers, and warehouse structures. - Leverage industry standards such as FHIR resources for canonical healthcare models. - Provide guidance on data modeling and warehouse best practices across both GCP and AWS ecosystems. Data Quality, Observability & Governance - Implement data quality frameworks, automated testing, and monitoring. - Ensure HIPAA compliance and proper handling of PHI/PII across all pipelines and cloud environments. - Drive lineage, documentation, metadata governance, and dbt docs adoption. Leadership & Collaboration - Partner with analytics, product, clinical informatics, and security teams to deliver high-quality, trustworthy data products. - Provide oversight and technical direction for multi-cloud data integrations with AWS-based systems or partners. - Assist in the recruitment and development of junior data engineers

• Architect, build, and scale healthcare data platform • Lead design and evolution of cloud-native data platform on GCP • Build scalable ETL/ELT pipelines using dbt and Airflow • Design and maintain BigQuery datasets and warehouse structures • Implement data quality frameworks and ensure HIPAA compliance • Partner with analytics and product teams to deliver data products • Provide oversight and technical direction for multi-cloud integrations