Data Engineer

Perform is seeking a Senior Data Engineer to join their Analytics team. The role involves building and maintaining the data platform on Azure Databricks while collaborating with business stakeholders to understand requirements and drive data-driven decisions.

Responsibilities:

• Design, build, and operate the data platform on Azure Databricks: ingestion, transformation, storage, and serving layers that power analytics, AI models, and operational reporting
• Build and maintain data pipelines across the ecosystem: Salesforce, SQL Server, Snowflake, third-party sources, and the new cloud-native payments platform
• Engineer for quality and trust with validation checks, anomaly detection, lineage tracking, and documentation that ensure every downstream consumer can rely on the data
• Write clean, version-controlled, production-grade code. Think like a software engineer building a product, not a script runner maintaining jobs
• Partner directly with business stakeholders across physician growth, member services, finance, and operations to understand how data drives decisions and then build for those decisions, not for abstract requirements
• Act as a technical product owner for your domain areas: own the backlog, prioritize based on 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
• Close the loop: follow your data through to the dashboard, the model, or the operational workflow and validate that it's actually driving the outcome
• Use Claude Code and agentic development as your primary workflow: AI-driven pipeline generation, automated testing, rapid prototyping to ship at a pace that would be impossible with traditional approaches
• Build data infrastructure that is AI-ready: well-documented, semantically clear, and structured so that AI tools and agents can reason over it effectively
• Scout, evaluate, and adopt emerging AI tools and platforms that make the data team faster by separating real value from hype with hands-on testing
• Share what you learn. Document patterns, run demos, and help the broader team adopt AI-first workflows with confidence

Requirements:

• BS in Computer Science, Data Science, or related field; 6+ years in data engineering or a hybrid data engineering/analytics role
• Deep hands-on experience with Azure Databricks: notebooks, Delta Lake, Unity Catalog, and production-scale pipelines. Databricks 3+ years is non-negotiable
• Strong Python and SQL; experience with PySpark and distributed data processing. Python is essential for validating AI-generated logic
• Built and operated data pipelines that serve analytics, ML models, and operational systems, not just batch ETL jobs
• Worked directly with business stakeholders to define requirements, shape data products, and deliver measurable outcomes
• Active, daily use of AI coding tools (Claude Code, Copilot, or similar) as a force multiplier. Claude and AI tooling in active daily use (team already works this way)
• Strong communication skills with a track record of presenting technical work to non-technical audiences
• Power BI familiarity for serving downstream BI team
• Salesforce/CRM data integration experience, directly relevant to use case
• Experience with engagement scoring or machine learning model pipelines
• Familiarity with agentic development patterns and AI-assisted coding
• Background in healthcare or mission-driven industries
• US-based preferred, not a hard requirement