• Design, implement, and maintain data solutions within the product platform • Define and optimize data models for analytical, operational, and predictive use cases • Participate in data architecture and technical infrastructure decisions • Ensure quality, robustness, and maintainability through good CI/CD practices • Collaborate with product owners, developers, and stakeholders to translate business needs into technical solutions

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role involves serving as a senior technical leader responsible for shaping and managing the modern data ecosystem. Your contributions will directly impact our mission to enhance healthcare by delivering high-quality data products that support clinical and operational outcomes. - Design and evolve a scalable, secure, cloud-native lakehouse platform leveraging Databricks and Microsoft Fabric. - Define modeling patterns and governance frameworks across the data lifecycle. - Lead design reviews and guide teams in adopting scalable architectures. - Drive long-term platform strategy and evaluate emerging technologies. - Design and implement batch and streaming data pipelines for healthcare data sources. - Develop frameworks for ingestion, quality, lineage, metadata, and observability. - Produce clean, analytics-ready datasets for BI and machine learning. - Implement HIPAA-aligned access patterns and secure handling of PHI. - Architect Databricks workloads for reliability and efficiency. - Integrate Databricks and Microsoft Fabric with Azure services and enterprise systems. Qualifications - 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. - Proficiency with Databricks, Spark, Delta Lake, dbt, and Python. - Experience designing large-scale cloud data platforms (Azure preferred). - Familiarity with Data Engineering, Data Factory, and Lakehouse. - Advanced expertise in data platform architecture. - Ability to create modular, extensible frameworks for enterprise solutions. - Strong command of distributed data processing techniques. - Experience in HIPAA regulated environments and handling PHI. - Fluency in healthcare data compliance standards. - Technical leadership and mentorship capabilities. - Excellent communication skills with technical and clinical stakeholders. Requirements - 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. - Proficiency with Databricks, Spark, Delta Lake, dbt, and Python. - Experience designing large-scale cloud data platforms (Azure preferred). - Familiarity with Data Engineering, Data Factory, and Lakehouse. - Advanced expertise in data platform architecture. - Ability to create modular, extensible frameworks for enterprise solutions. - Strong command of distributed data processing techniques. - Experience in HIPAA regulated environments and handling PHI. - Fluency in healthcare data compliance standards. - Technical leadership and mentorship capabilities. - Excellent communication skills with technical and clinical stakeholders. Benefits - 100% remote work (ET or CT work hours preferred). - Collaborative and inclusive work environment. - Opportunities for professional growth and development. - Mission-driven culture focused on improving healthcare. - Diversity, equity, and inclusion in recruitment practices. - Access to cutting-edge technology and resources.

• Design, build, and maintain data pipelines and integrations that ingest, transform, and model data from enterprise systems • Develop robust SQL‑based data models, including dimensional and star schema designs, to support analytics and reporting • Implement and maintain semantic models that clearly define business logic and metrics • Work independently to own end-to-end technical delivery, from requirements through implementation and validation • Partner with Finance, Analytics, and Engineering stakeholders to translate business requirements into scalable technical solutions • Contribute to improving data quality, reliability, and maintainability across the platform • Participate in design reviews and collaborate on architectural decisions, while remaining primarily hands-on

• Build and maintain ETL/ELT pipelines in Azure Data Factory, integrating sources such as ERP/CRM/APIs/streaming into a Lakehouse (Delta Lake) and consumption layers. • Develop transformations and jobs in Databricks using Spark (PySpark/Scala) and SQL, ensuring performance, cost-efficiency, and reliability. • Perform data modeling (bronze/silver/gold) and provide datasets for consumption in Power BI and/or the DW/Lakehouse. • Act as a bridge between data engineering and BI, translating business requirements into analytical solutions. • Implement governance best practices (catalog, ownership, standards, access controls) and support compliance with LGPD. • Define and operate Data Quality, observability, and lineage with monitoring, alerts, and SLAs. • Automate infrastructure and deployments using Terraform and CI/CD. • Document solutions, design architecture, and participate in technical decisions and estimates.