• Design and build data pipelines that ingest, transform, and serve data reliably - in Snowflake, Microsoft Fabric, or both depending on the engagement • Architect data storage solutions - lakehouses, warehouses, schemas - that balance performance, cost, and maintainability • Implement data quality frameworks and monitoring to catch problems before they reach reporting layers • Collaborate with Power BI developers and analysts to ensure semantic models have the clean, well-structured data they need • Write production-grade SQL for transformations, stored procedures, and performance-critical workloads • Design and maintain version-controlled, CI/CD-enabled data workflows • Contribute to solution design conversations - you'll have a voice in how we approach client problems end-to-end • Work directly with clients to understand their data landscape and propose engineering strategies that fit

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role offers a high-impact opportunity to lead the architecture and management of enterprise healthcare data, shaping how clinical and operational insights are captured, modeled, and utilized. You will transform raw, fragmented healthcare data into trusted, actionable datasets that power patient care, risk adjustment, and financial decisions. This position combines hands-on technical execution with strategic leadership, bridging engineering teams and clinical operations to define scalable, robust data systems. You will mentor engineers, influence architecture standards, and guide the design of foundational data models and ontologies. The environment is collaborative, innovative, and mission-driven, providing a chance to directly shape the future of healthcare delivery through data excellence. - Architect and maintain core healthcare data models, including patient, claims, risk, and attribution schemas, ensuring historical accuracy, scalability, and performance. - Define technology strategy and standards for data warehousing, transformation, and real-time processing, including dbt, Snowflake, and streaming pipelines. - Implement deterministic and probabilistic patient matching logic to unify fragmented data from EMRs, HIEs, and payer sources. - Serve as the primary interface between clinical/business operations and engineering teams, translating complex requirements into precise architectural specifications. - Mentor and guide engineers on healthcare data nuances, systemic standards, and best practices to raise technical excellence across the team. - Drive end-to-end execution of complex data initiatives, including SQL and Python pipeline development, dimensional modeling, and architecture reviews. Qualifications - Deep expertise in healthcare “payvider” domains, including provider and payer data, claims lifecycle, and standards such as EDI, FHIR, and ADT. - Proven experience designing scalable, enterprise-grade data platforms and architectures from the ground up. - Strong knowledge of column-oriented databases (e.g., Snowflake), real-time processing, partition pruning, and performance optimization at scale. - Expertise with dbt for transformation, including macroarchitecture, incremental strategies, dependency management, and quality control. - Hands-on experience in SQL and Python, with the ability to influence, review, and mentor engineers on complex pipelines. - Familiarity with dimensional modeling (Kimball/Star Schema), data normalization/denormalization strategies, and Medallion architecture. - Strong analytical, problem-solving, and communication skills, with the ability to bridge technical and clinical stakeholders. - Bachelor’s degree in Computer Science, Information Systems, or related field; advanced degree preferred. Benefits - Competitive salary and equity opportunities. - Comprehensive healthcare coverage, including medical, dental, and vision insurance. - Flexible and collaborative work environment with professional development opportunities. - Opportunity to shape innovative healthcare systems and financial models. - Transparent, mission-driven organizational culture with a focus on inclusion and collaboration. - Robust benefits package, including retirement plans and growth resources. Company Description

Who We Are Atlan is building the missing context layer for data and AI, helping enterprises close the AI value chasm. Today, 95% of AI pilots fail because AI systems don’t understand the context behind data: what it means, how it’s governed, and how it should be used. Atlan connects to every part of the modern data and AI stack to unify this context into a single, shared layer that both humans and AI agents can rely on. With Atlan, teams can discover, understand, and trust their data; build and collaborate on a shared body of knowledge; and activate that context across analytics, operations, and AI workflows.Trusted by global enterprises like Mastercard, Workday, General Motors, Unilever, Ralph Lauren, FOX, Nasdaq, and Medtronic, we’re backed by world-class investors including GIC, Insight Partners, Meritech, Peak XV, and Salesforce Ventures About the Role Atlan is building the context layer for AI agents and applications - transforming how data platforms power the next generation of AI. We're looking for a Staff and Principal Engineer to help architect and scale the foundational data plane that makes it easy to build apps, agents, and solutions for the AI era. You'll work on systems handling billions of assets, serving 100K+ users, with 99.99% availability targets. This is a high-agency role where you'll define technical direction, drive multi-quarter initiatives, and pioneer AI-native development practices. What you will do 🤔 - Design and build platform services—APIs, infrastructure components, runtime systems, and ingestion frameworks—at enterprise scale - Architect the context store that transforms lakehouse infrastructure into AI-ready systems with multimodal capabilities (structured, unstructured, vector, graph) - Solve complex multi-tenant isolation and scaling problems for enterprise SaaS - Design data contracts governing ingestion, validation, processing, routing, storage, and serving across heterogeneous systems - Own critical shared infrastructure including lakehouse (Iceberg/Polaris), vector stores, graph databases, and OLTP systems - Drive technical standards through RFCs, architecture reviews, and documentation - Mentor senior engineers and influence architecture decisions across teams - Write production code using AI-assisted development tools (Claude Code, Cursor) - Debug distributed systems issues across Kubernetes, workflow orchestration, and microservices What makes you a match? 😍 Must Have - 8+ years in platform engineering, infrastructure, or backend systems at a SaaS company - Experience building enterprise-scale distributed systems at scale - Deep expertise in multi-tenant architectures and tenant isolation strategies - Strong Kubernetes, containerization, and cloud infrastructure skills (AWS/GCP/Azure) - Hands-on experience with distributed systems patterns—service mesh, event-driven architecture, orchestration - Track record of driving multi-quarter technical initiatives from concept through production at scale Deep Expertise in One or More - Lakehouse architectures - Vector stores - Graph databases - Streaming systems Strong to Have - Experience designing contract-driven or schema-first data platforms - Familiarity with Temporal or similar workflow orchestration systems - Data quality frameworks, observability systems, and cost attribution at scale - Experience supporting enterprise workloads with strict compliance requirements - CI/CD pipeline design and GitOps practices Who You Are - You embrace AI-native development and want to pioneer new engineering workflows - You have high agency and take ownership of ambiguous problems - You're a strong async communicator who can influence without authority - You're comfortable with fast-changing priorities in a scale-up environment - You act as a force multiplier—elevating the technical bar for those around you Team & Location (Remote) You'll join a distributed team (India/Australia/US Remote) working closely with two distinguished engineers leading the data platform charter. We operate in weekly sprints with strong emphasis on ownership, velocity, and AI-native practices. More About Us Atlan is building the shared context layer that enterprises need so AI can operate on trusted, governed context. The conversation has moved from data leaders asking: “Can we trust the data in our stack?” to businesses asking: “Can we trust AI inside the business?” We are the missing infrastructure for businesses becoming AI-forward - the connective tissue between their data stack, operational systems, and AI agents. Recognized as an industry-leading metadata, catalog, and data governance platform, we’ve been named a Leader by both Gartner and Forrester across enterprise data catalogs, metadata management, and governance. To learn more, visit www.atlan.com and follow us on LinkedIn Equal Opportunity Employer Atlan is committed to building an inclusive, diverse, and authentic workplace. We do not discriminate based on race, color, religion, national origin, age, disability, sex, gender identity or expression, sexual orientation, marital status, military or veteran status, or any other legally protected characteristic.

What started as a small group of families gathered around a kitchen table in 1979 has blossomed into the nation's leading voice on mental health. The National Alliance on Mental Illness (NAMI) is the nation's largest grassroots mental health organization dedicated to building better lives for the millions of Americans affected by mental illness. Today, we are an alliance of hundreds of local affiliates, state organizations and a national office that work in communities across the United States to raise awareness and provide support and education that was not previously available to those in need. NAMI advocates for all who are affected by mental illness, both the individuals and the people in their lives. We work to address disparities and injustices and to promote dignity and inclusion for all people with mental illness and their families. In addition to being advocates, we educate, we listen, and we lead as evidenced by our public awareness campaigns, the range of programs we provide, and our strong public policy. We currently have an opening for a Senior Data Engineer. As NAMI's first dedicated Data Engineer, the individual in this role will architect and build our data pipeline from the ground up, balancing near-term reporting needs with a durable, scalable foundation for future growth. The Senior Data Engineer is part of the Software Engineering team responsible for building and operating data pipelines, curated datasets, and reporting foundations that enable NAMI to measure program impact, support affiliate reporting, and drive data-informed decisions. This role partners closely with Engineering, Salesforce, Programs & Services, Success Team, and the Data Team to improve data quality, reduce manual reporting workflows, and deliver trusted, well-documented data products. This full-time position is remote. Salary Range: $95k - $105k ESSENTIAL DUTIES AND RESPONSIBILITIES: - Design, build, and maintain scalable ELT/ETL pipelines integrating data from operational systems, program reporting tools, forms, and third-party services - Build and maintain canonical datasets and data models that support consistent reporting across program types and implementations - Implement data quality checks, validation rules, and automated monitoring/alerting for critical data products - Develop and maintain transformations using analytics engineering best practices (e.g., modular, testable, well-documented SQL/Python), including models, tests, and documentation - Configure and support automated job runs via a CI/CD pipeline (e.g. Github Actions) to enforce testing and promote reliable releases - Partner with stakeholders to translate reporting needs into clear data requirements and maintainable implementations - Improve data accessibility through role-based access controls and curated data marts for dashboards and self-service analytics - Create and maintain documentation (data definitions, lineage, runbooks) for both technical and non-technical audiences - Optimize pipeline reliability, performance, and cost across storage, compute, and orchestration (including optimizing workloads, file layouts, and incremental processing patterns) - Orchestrate and monitor workflows, including retries, alerting, and runbooks - Work with tooling for observability, uptime, and performance monitoring - Be a collaborative colleague who learns from and educates their team - Proactively communicates, maintains expectations, and keeps work records up to date in project management systems and team communication channels - Stay current with industry trends and identify new ways for the team to improve - Other duties as assigned MINIMUM QUALIFICATIONS: - 5+ years of experience in data engineering, analytics engineering, or backend engineering with significant data ownership - String proficiency in Python (preferred) or another programming language used for data engineering - Strong SQL skills and experience building production-grade data models for reporting and analytics - Experience using managed or self-hosted EL tools such as Airbyte or Fivetran to ingest and maintain sources - Experience building analytics platforms using a modern data warehouse (e.g., MotherDuck, BigQuery, Redshift, Databricks, Snowflake), and a modern data lake (e.g., Amazon S3, Azure Blob Storage) - Hands-on experience with transformation tools (dbt Core or equivalent) - Experience in modeling, testing, monitoring, and building CI/CD for data pipelines and transformations (including dbt tests and deployment practices) - Experience with orchestration/workflow tools (e.g., Airflow, Dagster, Prefect) and scheduling reliable jobs - Experience running transformation jobs in CI/CD tooling such as GitHub Actions (e.g., PR validation, scheduled runs, environment-based deployments) - Experience with data visualization tools such as Tableau, Looker, Power BI, or Metabase - Familiarity with access controls and least-privilege practices for datasets and reporting layers, including managing data access across teams and roles - Hands-on experience with Docker - Strong proficiency in Git/GitHub - Strong debugging skills and ability to troubleshoot data issues end-to-end (source → pipeline → model → dashboard) - Excellent communication and collaboration skills; comfort working cross-functionally with non-technical partners - Comfort in a remote-first workplace - Well organized team player - Must pass background check Your salary is only one component of the total compensation package. NAMI offers a range of standard and unique benefits that are reviewed annually: - Generous and comprehensive Health, Dental, and Vision Plans - Paid Time Off: Vacation, Personal, and Sick Leave - Paid Parental Leave - 403(b) retirement plan - Flexible Spending Accounts for health care, dependent care and commuter expenses - Life Insurance and Disability coverage paid by NAMI - Flexible Work and Telework programs - Professional Development Reimbursement program - A variety of wellness offerings to support team members - Employee Referral Program - The Employee Assistance Program (EAP) which provides support for personal and family problems common in contemporary life NAMI is proud to be an equal opportunity employer and is committed to creating a diverse and inclusive workforce. NAMI prohibits discrimination and harassment against any employee or applicant for employment because of race, color, religion, sex, national origin, marital status, age, disability, veteran status, sexual orientation, gender identity or expression, pregnancy, childbirth or related medical conditions, genetic information or any other legally protected group status. We also provide reasonable accommodation for candidates with disabilities.