Role Description We are looking for a highly technical and hands-on Senior Healthcare Data Modeler to lead a global data modeling team responsible for transforming conceptual and logical data models into optimized physical models. This role requires a deep understanding of healthcare data domains (Claims, Pharmacy, Membership, Enrollment, Provider, Billing, Interoperability - FHIR, HL7, ADT, CCDA, etc.) and expertise in enterprise data modeling best practices. The Senior Healthcare Data Modeler will work closely with the Director of Data Modeling to define data modeling standards, metadata governance, and data architecture strategies that align with business and technical needs. They will also collaborate with engineering, client implementation teams, and data governance teams to ensure models support operational and analytical use cases, high performance, and data integrity. This is an exciting opportunity for a technical leader with expertise in Databricks, Snowflake, schema evolution, and modern data engineering practices to shape the future of healthcare data platforms. Your day to day - Data Modeling Leadership & Best Practices - Lead a global team of data modelers to build scalable, high-performance physical data models aligned with enterprise architecture and industry standards. - Define and enforce data modeling best practices, including schema evolution, lossless data modeling, metadata management, and data versioning. - Convert conceptual and logical data models into optimized physical models using enterprise data modeling tools (Erwin, ER/Studio, DBT, or similar). - Ensure models support analytical (data science, actuarial, reporting) and operational (Claims, Care Management) use cases. - Maintain metadata, business glossary, and data dictionaries to support data lineage and governance. - Implement data quality rules and validation frameworks to meet industry SLAs and compliance requirements (HIPAA, HITRUST, CMS regulations). - Technical Data Modeling Engineering - Own data modeling workflows, including version control, schema deployment, and upgrade automation. - Own metadata tagging and data quality rules implementation for healthcare data domains. - Work with engineering teams to implement data models in Databricks, Snowflake, and modern cloud architectures. - Optimize data models for query performance, indexing, partitioning, and storage efficiency. - Ensure data enrichment (standardization, transformations, algorithms, and grouping) is seamlessly integrated into the data models. - Design and document data delivery patterns using complex events or business rules to enable real-time and batch processing. - Implement data federation strategies, ensuring interoperability between enterprise data lakes, cloud data warehouses, and transactional systems. - Collaboration & Client Engagement - Collaborate with Director of Data Modeling and client implementation teams to understand client needs, assess data model impact, and enhance models for efficient implementations. - Partner with engineering and product teams to define and prioritize data model features in the product roadmap. - Work with client management teams to provide support and technical expertise during client engagements. Qualifications - 8+ years of experience in data modeling, enterprise data architecture, and cloud-based data engineering. - 4+ years leading data modeling teams in enterprise environments. - Expertise in enterprise data modeling tools (Erwin, ER/Studio, DBSchema, or similar). - Strong hands-on experience with cloud data platforms (Databricks, Snowflake) and data lakehouse architectures. - Deep understanding of healthcare data models across Core Payer (Claims, Pharmacy, Membership, Enrollment, Provider, Billing, Premium) and Clinical Interoperability (FHIR, HL7, ADT, CCDA, etc.) domains. - Experience with schema evolution, lossless data modeling, and data versioning strategies. - Strong background in SQL, performance tuning, and query optimization for large-scale datasets. - Hands-on experience with data engineering workflows, including data pipeline integration, ELT/ETL optimizations, and data transformation frameworks. - Ability to mentor junior modelers, enforce modeling best practices, and drive adoption of enterprise data standards. - Excellent collaboration skills to work with data architects, engineers, and client implementation teams. Requirements - Experience with OBT, data Lakehouse, and metadata management frameworks. - Familiarity with business intelligence (BI) and reporting solutions on modern cloud data warehouses. - Knowledge of APIs, SDKs, and data distribution frameworks for enabling real-time and batch data access. - Experience with data governance, security, and regulatory compliance in healthcare data management. Benefits - Unlimited paid time off – recharge when you need it - Work from anywhere – flexibility to fit your life - Comprehensive health coverage – multiple plan options to choose from - Equity for every employee – share in our success - Growth-focused environment – your development matters here - Home office setup allowance – one-time support to get you started - Monthly cell phone allowance – stay connected with ease Our Commitment as an Equal Opportunity Employer As a mission-led technology company helping to drive better healthcare outcomes, Abacus Insights believes that the best innovation and value we can bring to our customers comes from diverse ideas, thoughts, experiences, and perspectives. Therefore, we dedicate resources to building diverse teams and providing equal employment opportunities to all applicants. Abacus prohibits discrimination and harassment regarding race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. At the heart of who we are is a commitment to continuously and intentionally building an inclusive culture—one that empowers every team member across the globe to do their best work and bring their authentic selves. We carry that same commitment into our hiring process, aiming to create an interview experience where you feel comfortable and confident showcasing your strengths. If there’s anything we can do to support that—big or small—please let us know.

• Desenhar, implementar e manter pipelines de dados robustos e eficientes. • Desenvolver e otimizar fluxos de ingestão e transformação de dados em lote e streaming. • Estruturar nosso ecossistema utilizando Iceberg para garantir performance e consistência. • Ajustar consultas complexas no Trino para garantir latência mínima aos usuários internos e ferramentas de BI. • Desenvolver soluções distribuídas utilizando Spark que suportem o crescimento da Octadesk. • Garantir a integridade, confiabilidade e disponibilidade dos dados que alimentam decisões.

Role Description We’re looking for a Staff Data Engineer to define the architecture and long-term evolution of Syndio’s data platform as we scale into AI-driven products. This role owns the technical direction for how data is structured, standardized, modeled, and activated across the company, with a particular focus on enabling LLM-powered systems and decisioning workflows. - Design, architect, and build data pipelines that produce the data products powering analytics, product experiences, and Syndio's LLM-powered systems. - Shape how data is used across the company and enable the development of Syndio's decision graph and AI capabilities. - Solve complex challenges such as: - Multi-tenant schema standardization - Automated field mapping - Retrieval-ready data modeling - Data pipelines for AI evaluation and feedback loops - Operate across pipeline architecture, platform design, and cross-functional data strategy, partnering closely with product, data science, and engineering leadership. Qualifications - Deep experience in data engineering, data platform, or distributed systems roles. - Proven track record designing and scaling data pipelines across teams or organizations. - Experience solving ambiguous, high-impact platform problems and building systems widely adopted across teams. - Strong ability to operate at both architectural and hands-on levels. - Cross-functional leadership and ability to influence technical direction. Requirements - Data modeling and schema design. - Pipeline architecture and orchestration. - Data warehouse and storage systems. - Strong proficiency in Python and SQL. - Experience with data patterns for AI/LLM systems: - Retrieval-ready data modeling - Vector storage - Embedding pipelines - Evaluation datasets - Cloud platforms: GCP (preferred), AWS, or Azure. - Cloud data warehouses: BigQuery, Snowflake, Redshift, or similar. - Data transformation: dbt or equivalent modern transformation frameworks. - Relational databases: Postgres, MySQL, or similar. - Streaming and CDC ingestion patterns (Datastream, Debezium, Kafka, or similar). - Data governance and lineage tooling. Benefits - Competitive Compensation: Base salary targeted at $180-195k CAD. - Syndio Equity: Share in Syndio’s success. - 20 days annually for vacation, plus paid sick & safe time, compassion leave, and voting leave. - Pension Contribution. - Remote-First with opportunities for local meet-ups in Calgary. Role Progression - By the 3rd Month: Working knowledge of Syndio's data domain and existing pipeline architecture; hands-on contributions to the data layer codebase. - By your 6th Month: Technical ownership of the AI data infrastructure; defining architectural direction for data modeling and serving to AI systems. - By 9 months in: Set the long-term technical direction for Syndio's data platform and AI-driven systems; mentor other engineers. The Interview Overview - 30 min - Phone call with a member of our Talent Team. - 45 min - Zoom interview with the Hiring Manager. - Technical Evaluation - Take-home. - 1.5 hrs - Meet the Team. - 30 min - Final Interview.

• Data Modeling: Develop, document and maintain models in dbt/SQL (Dev, Staging, Prod), ensuring traceability and versioning. • Data Quality: Apply automated tests to ensure maximum reliability of the holding's KPIs. • Pipeline Architecture: Monitor ETL/ELT flows, ensuring resilience in API consumption and ingestions via Airbyte. • Governance and Catalog: Keep models and field documentation up to date, following strict naming standards. • Business Intelligence: Translate complex business needs into automated dashboards and reports in Metabase. • Strategic Partnership: Collaborate with stakeholders to define and monitor operational and financial KPIs.