Senior Data Engineer

Perform

Senior Data Engineer developing and maintaining data platforms on Azure for MDVIP. Collaborating with business teams to innovate data-driven solutions impacting healthcare services.

Posted 4/22/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

Tech Stack

Tools & technologies

AzureCloudETLGoPySparkPythonSQLUnity

About the role

Key responsibilities & impact

Own the Data Platform
Design, build, and operate MDVIP’s 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 MDVIP’s ecosystem: Salesforce, SQL Server, Snowflake, third-party sources, and the new cloud-native payments platform.
Engineer for quality and trust—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.
Go Deep Into the Business Domain
Partner directly with business stakeholders across physician growth, member services, finance, and operations to understand how data drives decisions—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.
Drive AI-First Engineering Practices
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—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

What you’ll need

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.
Strong Python and SQL; experience with PySpark and distributed data processing.
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.
Strong communication skills with a track record of presenting technical work to non-technical audiences.

Benefits

Comp & perks

Health insurance
Retirement plans
Paid time off
Flexible work arrangements
Professional development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

data engineeringAzure DatabricksPythonSQLPySparkdata pipelinesDelta LakeUnity Catalogdistributed data processingAI-driven pipeline generation

Soft Skills

communication skillscollaborationproblem-solvingstakeholder engagementtechnical product ownershipiterative developmentdocumentationpresentation skillsbusiness acumenadaptability

Certifications

BS in Computer ScienceBS in Data Science