Forward Deployed Data Engineer

Perform is seeking a Senior Forward Deployed Data Engineer to build and operate an Azure Databricks data platform while partnering directly with healthcare business stakeholders. The role owns data architecture, pipelines, AI-ready infrastructure, technical product priorities, and end-to-end delivery of data solutions across physician growth, member services, finance, and operations.

Responsibilities:

• Design, build, and operate the data platform on Azure Databricks, covering ingestion, transformation, storage, and serving layers that power analytics, AI models, and operational reporting
• Build and maintain pipelines across the business ecosystem, including Salesforce, SQL Server, Snowflake, third-party sources, and a new cloud-native payments platform
• Engineer for quality and trust through validation checks, anomaly detection, lineage tracking, and documentation that every downstream consumer can rely on
• Write clean, version-controlled, production-grade code. Think like a software engineer building a product, not a script runner maintaining jobs
• Own architecture decisions across storage, transformation, orchestration, and serving, balancing delivery speed against long-term maintainability
• Partner directly with stakeholders across physician growth, member services, finance, and operations to understand how data drives decisions, then build for those decisions rather than for abstract requirements
• Act as technical product owner for your domain areas. Own the backlog, prioritize by business impact, and ship iteratively without waiting for a PM to sequence your work
• Translate ambiguous business questions into data models, feature tables, and curated datasets that analysts and data scientists can build on immediately
• Push back on vague requirements until they are sharp enough to build against
• Close the loop. Follow your data through to the dashboard, the model, or the operational workflow and validate that it is actually driving the outcome
• Communicate tradeoffs, limitations, and timelines clearly to technical and non-technical audiences alike
• Use Claude Code and agentic development as your primary workflow, including AI-driven pipeline generation, automated testing, and rapid prototyping, to ship at a pace traditional approaches cannot match
• Build data infrastructure that is AI-ready: well-documented, semantically clear, and structured so that AI tools and agents can reason over it effectively
• Support data workflows behind AI and LLM applications, including curated context, retrieval-ready datasets, and the evaluation data needed to measure whether those systems are working
• Scout, evaluate, and adopt emerging AI tools and platforms that make the data team faster, separating real value from hype through hands-on testing
• Share what you learn. Document patterns, run demos, and help the broader team adopt AI-first workflows with confidence
• Apply appropriate handling, access control, and compliance practices for sensitive and regulated healthcare data

Requirements:

• US residency is required
• • BS in Computer Science, Data Science, or a related field, with 6 or more years in data engineering or a hybrid data engineering and analytics role
• • Deep hands-on experience with Azure Databricks, including notebooks, Delta Lake, Unity Catalog, and production-scale pipelines
• • Strong Python and SQL, with experience in PySpark and distributed data processing
• • Experience building and operating pipelines that serve analytics, ML models, and operational systems, not only batch ETL jobs
• • Direct experience working with business stakeholders to define requirements, shape data products, and deliver measurable outcomes
• • Active, daily use of AI coding tools as a force multiplier, with Claude Code as the primary platform and familiarity across other leading models
• • Data architecture experience, including designing systems rather than only implementing against someone else's design
• • Experience building data products for marketing or growth teams, such as customer segmentation, campaign attribution, or engagement and retention analytics
• • Strong communication skills and a track record of presenting technical work to non-technical audiences
• • Proven ability to take an ambiguous business problem and turn it into a working data solution with minimal direction
• • Healthcare, PHI, or other regulated-industry data experience
• • Experience with Salesforce data models and integrations
• • Experience with payments platforms or financial data pipelines
• • Experience with MCP, agent architectures, RAG, embeddings, or vector databases
• • Experience with data governance, cataloging, access control, and compliance frameworks
• • Experience with LLM evaluation, observability, prompt optimization, and AI system cost management
• • Experience with BI and visualization tooling, or building analytics for non-technical users
• • Experience in a forward-deployed, consulting, client-facing, startup, or highly autonomous engineering environment