• The Cloud Data Architect serves as the senior technical authority responsible for defining, governing, and evolving enterprise architecture to support mission and business objectives • Leads the development of architecture strategies, standards, and technical roadmaps • Ensures alignment with enterprise architecture frameworks and federal best practices • Provides architectural oversight across engineering, product development, infrastructure, data platforms, and digital services • Develops and maintains enterprise architecture framework, reference architectures, and technology standards • Establishes and leads architecture governance processes • Maintains enterprise architecture artifacts • Defines and maintains reference architectures for cloud environments, SaaS platforms, enterprise applications, and data services • Guides engineering and development teams in designing integrated, scalable, and resilient solutions • Develops and maintains multi-year technology roadmaps aligned with organizational goals

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description We are seeking a visionary and hands-on Data Engineering Lead to spearhead the design, development, and optimization of our next-generation data platform. As a Lead, you will balance technical excellence with people leadership, ensuring our data architecture is scalable, resilient, and perfectly aligned with our business goals while collaborating cross-functionally to support analytics, reporting, and operational data needs. The ideal candidate should be a PySpark expert who thrives in the Azure ecosystem and has a deep appreciation for clean, modular code and robust ETL patterns. This is an exciting opportunity to work along with a great team of data engineers, demanding technologies, and an engaging work environment to help shape our data engineering best practices. Key Responsibilities - Technical Leadership: Design and supervise the implementation of comprehensive data pipelines utilizing Azure Databricks and PySpark. - Team Mentorship: Direct a team of data engineers, performing code reviews, offering technical expertise, and cultivating a culture of ongoing learning. - Data Modeling & Optimization: Develop high-performance schemas in PostgreSQL and refine complex SQL queries for large datasets. - ETL Strategy: Establish and apply optimal practices for data ingestion, transformation, and storage (Delta Lake/Lakehouse patterns). - Strategic Collaboration: Collaborate closely with Data Analysts, Architects, and Product Managers to convert business requirements into technical specifications. - Process Improvement: Promote the implementation of CI/CD, unit testing, and automated monitoring to achieve 99.9% data reliability. - Ensure data quality, governance, and compliance through validation, documentation, and secure practices. - Continuously improve data systems for enhanced performance, reliability, and scalability. - Effectively engage within an agile, cross-functional project team. Mandatory Skillset - Azure Databricks: - Expert-level experience managing workspaces, clusters, and job scheduling. - Solid understanding of data lakehouse architectures and Delta Lake. - Proven experience in Performance Tuning, Spark Optimization and Cost Reduction. - PySpark: Advanced proficiency in Spark DataFrame APIs and Spark SQL for large-scale data processing involving various data formats. - SQL Mastery: Exceptional ability to write, tune, and troubleshoot complex queries. - PostgreSQL: Hands-on experience with relational database design, indexing, and performance optimization. - ETL/ELT Frameworks: Proven track record of building scalable data pipelines from scratch. Desired Skills - Workflow Orchestration: Experience with Apache Airflow for managing complex task dependencies. - Containerization: Familiarity with Azure Kubernetes Service (AKS) for deploying containerized data services. - Infrastructure as Code (IaC): Knowledge of Terraform or Bicep for managing Azure resources. Qualifications - 10+ years of experience in Data Engineering or Software Engineering. - 3+ years as a formal technical Lead managing an agile team and implementing E2E solutions. - Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. - Strong communication skills with the ability to explain complex technical concepts to non-technical stakeholders. - Strong problem-solving skills and attention to detail. Benefits - You won't just be "maintaining" pipelines; you'll be the primary architect of a data ecosystem that powers real-time decision-making across the entire organization.

Are you ready to power the World's connections? If you don’t think you meet all of the criteria below but are still interested in the job, please apply. Nobody checks every box - we’re looking for candidates that are particularly strong in a few areas, and have some interest and capabilities in others. We're looking for a Senior Data & AI Engineer to join our Revenue Analytics team. In this role, you'll own both the data infrastructure and AI systems that power revenue insights and intelligent experiences across the business. You'll be responsible for building and maintaining reliable pipelines and scalable data models and determining how we connect those data assets to AI tools — which solutions we invest in, and how we do it securely and cost-effectively. You'll sit at the intersection of data engineering, AI/ML, and platform architecture, with high visibility and real impact on our long-term data and AI strategy. What You'll Do Data Infrastructure & Pipelines - Design, build, and maintain ETL/ELT pipelines using Fivetran + Snowflake integrations to ingest data from a variety of sources into our Snowflake data warehouse - Develop and manage robust data models in Snowflake, ensuring data is structured for performance, reliability, and ease of use by analysts and business stakeholders - Use Hightouch to operationalize data by syncing warehouse data to downstream CRM, marketing, and sales tools - Monitor pipeline health, troubleshoot data quality issues, and implement alerting to proactively catch failures - Document data models, pipelines, and lineage to support a culture of data literacy and self-service analytics AI Infrastructure & Integrations - Integrate Claude and other LLMs directly with our Snowflake data warehouse, enabling AI-powered querying, summarization, and insight generation on top of live revenue data - Build and maintain data sources, semantic layers, and search services within Snowflake Cortex and connected AI platforms - Design and deploy AI agents that can reason over structured and unstructured revenue data to support go-to-market workflows Agent Orchestration - Architect and manage multi-step agent workflows, coordinating across tools, APIs, and data sources to automate complex analytical and operational tasks - Evaluate and implement orchestration frameworks (e.g., LangChain, LlamaIndex, or custom solutions) best suited to our use cases AI Strategy & Evaluation - Run rigorous evaluations of AI tools, models, and platforms to determine the best solution for each use case (e.g., Snowflake Cortex vs. Claude vs. Gemini vs. custom fine-tuned models) - Develop evaluation frameworks covering quality, latency, cost, and security to inform build vs. buy decisions and guide our overall AI roadmap - Stay current on the rapidly evolving AI landscape and proactively recommend new tools or approaches as the space matures Security & Governance - Implement and manage row-level security (RLS) in Snowflake to ensure AI tools only surface data that users are authorized to see - Maintain and evolve role-based access controls (RBAC) alongside new RLS policies - Contribute to data governance practices, including access controls, PII handling, and schema management - Partner with Data, Security, and Legal teams to establish AI data governance standards and guardrails Cost Management & Optimization - Monitor and manage AI credit consumption across Snowflake Cortex, API usage, and other platforms to keep spending within budget - Identify and implement optimizations — such as caching, prompt tuning, model selection, and query efficiency improvements — to reduce cost without sacrificing quality - Build reporting to give stakeholders visibility into AI spend and usage trends Cross-Functional Partnership - Partner with Revenue Operations, Finance, and Sales to understand data needs and translate them into scalable engineering solutions - Collaborate across technical and non-technical teams to deliver data and AI solutions that directly influence revenue strategy What We're Looking For - 3+ years of experience in a data engineering, analytics engineering, or AI/ML engineering role - Hands-on experience with Snowflake, including data modeling, query optimization, Cortex Analyst, Cortex Search, semantic layers, and security model (RBAC, RLS) - Proficiency with Fivetran for pipeline orchestration and connector management - Experience with Hightouch or similar reverse ETL tools for syncing data to operational systems - Experience integrating LLMs (Claude, GPT-4, or similar) into production data workflows via API - Familiarity with agent orchestration frameworks and patterns (e.g., LangChain, LlamaIndex, CrewAI, or custom implementations) - Strong understanding of AI/LLM evaluation methodologies — you know how to measure whether an AI solution is actually working - Experience with prompt engineering, retrieval-augmented generation (RAG), and/or fine-tuning - Strong SQL and Python skills - A security-first mindset with experience managing data access controls in cloud data platforms - Strong communication skills and the ability to collaborate across technical and non-technical teams Nice to Have - Experience with Anthropic's Claude API or Claude for Enterprise - Familiarity with vector databases (e.g., Pinecone, Weaviate, or Snowflake's native vector support) - Experience with dbt for data transformation - Experience with a workflow orchestration tool such as Airflow or Prefect - Familiarity with data observability tools (e.g., Monte Carlo, Metaplane) - Background supporting Sales, Finance, or Revenue Operations use cases - Experience building internal AI tools or copilots for business teams Why Join Us - Shape our data and AI strategy from the ground up — this is a greenfield opportunity with real ownership - Work on data that directly influences revenue strategy and business decisions - Work at the cutting edge of enterprise AI on a team that's fully bought in on building with AI responsibly - High cross-functional visibility across Revenue, Finance, Product, and Engineering - Competitive salary, equity, and benefits About Kong: Kong Inc., a leading developer of API and AI connectivity technologies, is building the infrastructure that powers the agentic era. trusted by the Fortune 500 and startups alike, Kong's unified API and AI platform, Kong Konnect, enables organizations to secure, manage, accelerate, govern, and monetize the flow of intelligence across APIs and AI models. For more information, visit www.konghq.com.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description Qualified Health is seeking a Data Engineer to support partner integration. This isn't a role where you'll maintain legacy systems or write reports nobody reads. You'll build the data pipelines that directly power AI systems used by clinicians across major U.S. health systems — systems that help doctors identify the right diagnosis, flag patients who need intervention, and improve outcomes at scale. Every query you write, every pipeline you build, directly touches patient care. You'll work in a pod with a Director who owns the customer relationship and 1-2 peers who share the mission. The path from Senior Data Engineer to Director of your own pod is clear and real — we're growing fast enough that the opportunity is a matter of when, not if. Key Responsibilities - Build and maintain data ingestion pipelines that pull from Epic Clarity databases, FHIR APIs, and HL7 feeds into our Databricks lakehouse architecture. - Execute data quality validation and mapping verification for each new workflow deployment. - Support 2-4 health system partners as part of a dedicated integration pod. - Develop and optimize SQL queries against complex healthcare data models. - Contribute to defining reusable data transformation patterns that accelerate future partner onboarding. Qualifications - 4+ years as a data engineer building production ETL/ELT pipelines. - Strong SQL skills (complex joins, window functions, CTEs across large healthcare datasets). - Proficiency with Python and PySpark for data transformation at scale. - Experience with cloud data platforms (Databricks preferred; Snowflake, BigQuery, or Redshift accepted). - Familiarity with FHIR data models or willingness to learn healthcare data standards quickly. - Azure cloud services experience (ADLS Gen2, Key Vault, networking basics). - Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or related technical field. Desired Skills - Epic Clarity/Caboodle experience or healthcare data background. - Delta Lake, Delta Sharing, or lakehouse architecture experience. - HL7 message processing or real-time healthcare data pipelines. - Experience working in a multi-tenant SaaS environment. Technical Environment - Azure Databricks + Data Factory (plus Fabric and Snowflake integrations). - PySpark for distributed data processing. - GitHub Actions + Terraform for CI/CD and Infrastructure as Code. - Python with type-safe patterns and modern frameworks. - Healthcare data formats including FHIR, Epic Clarity, and other EHR schemas. What Success Looks Like - High-quality data pipelines delivered on schedule with thorough testing and documentation. - Proactive issue identification with technical problems caught and resolved before impacting partners. - Reusable components that reduce implementation time for subsequent integrations. - Clean production deployments with minimal post-launch issues. - Technical credibility with partner IT teams based on quality of work. - Efficient troubleshooting with quick diagnosis and resolution of data quality issues. Impact & Growth Opportunity As a Data Engineer at Qualified Health, you'll build the data infrastructure that powers AI-driven insights for major health systems. Your work directly enables better patient care by ensuring high-quality, reliable data flows into clinical decision support tools. This role offers deep technical learning in healthcare data, exposure to diverse health system architectures, and growth potential into senior technical or platform architecture roles as we scale. Why Join Qualified Health? - Mission that matters: We partner with the country's leading health systems to safely deploy AI that improves patient care, operational efficiency, and financial performance. - Serious traction: 14+ health system partners including the University of Texas system, University of Rochester Medical Center and Jefferson Health. - Public Benefit Corporation: We're organized to prioritize patient outcomes alongside business performance. - Comp that competes: We offer market competitive base salary, meaningful equity with real upside, and comprehensive benefits. Our Commitment to Diversity Qualified Health is an equal opportunity employer. We believe that a diverse and inclusive workplace is essential to our success, and we are committed to building a team that reflects the world we live in. Pay & Benefits The pay range for this role is between $155,000 and $190,000, and will depend on your skills, qualifications, experience, and location. This role is also eligible for equity and benefits. Join our mission to revolutionize healthcare with AI. To apply, please send your resume through the application below.