About Reveloop

Reveloop is building a modern data platform that brings together data from revenue cycle management, payer systems, client systems, financial platforms, applications, and internal products.We’re looking for a Data Engineer / Data Platform Engineer to take ownership of building and managing our data lake and broader data platform.

The Role

We are looking for a Data Engineer / Data Platform Engineer who can take meaningful ownership of building and managing Reveloop’s data lake and broader data platform. This is not a reporting-only or traditional ETL maintenance role. You will help design and operate our centralized data lake, with Azure Data Lake Storage Gen2 (ADLS Gen2) as the core historical data layer.

What You'll Do

• Design, build, and manage Reveloop’s data lake and data platform.
• Develop scalable ETL/ELT pipelines using SQL, Python, and modern data engineering tools.
• Build ingestion pipelines for databases, APIs, SaaS applications, files/SFTP, and other data sources.
• Establish Raw/Bronze, Staging/Silver, and Gold/curated data layers.
• Build reliable, testable, and replayable data pipelines with appropriate monitoring and recovery.
• Implement data quality, lineage, governance, security, and access controls.
• Design data models and curated datasets for RCM, payer analytics, claims, denials, finance, operations, and product analytics.
• Design the platform to appropriately protect PHI, PII, financial information, and other sensitive healthcare and business data.
• Establish data contracts and mechanisms for identifying and managing source-system schema changes.
• Help establish the foundation for analytics, automation, AI, and future products.
• Work closely with software engineers, product teams, analysts, finance, operations, and business stakeholders.
• Document architecture, pipelines, schemas, business logic, data lineage, runbooks, dependencies, and operational procedures.
• Design appropriate data partitioning, file organization, storage formats, and lifecycle policies for performance and cost management.
• Establish data lineage from source systems through transformations to final reports, applications, and downstream consumers.

What We're Looking For

• 5+ years of relevant data engineering or data platform experience, or equivalent demonstrated capability.
• Strong SQL and Python skills.
• Experience designing and operating production ETL/ELT pipelines.
• Hands-on experience with a data lake, lakehouse, or modern data platform.
• Strong understanding of data modeling, data warehousing, and database architecture.
• Experience with Azure, preferably Azure Data Lake Storage Gen2.
• Experience with data quality, governance, security, monitoring, and lineage.
• Experience with Git, code reviews, and CI/CD.
• Ability to troubleshoot production data issues and translate business requirements into scalable technical solutions.
• Strong understanding of data quality, data governance, security, access controls, and lineage.
• Strong communication skills and ability to work independently.

Nice To Have

Experience with:

• Azure Databricks, Microsoft Fabric, Synapse, Snowflake, or similar platforms
• Experience with dbt
• Airflow, Dagster, Prefect, or Azure Data Factory
• Delta Lake / Delta Tables
• CDC and event-driven architectures
• Terraform, Docker, and modern CI/CD
• Data catalog, metadata, lineage, and observability tools
• Healthcare, RCM, claims, payer, or financial data
• Analytics, machine learning, AI, or automation

Why This Role Matters

You’ll be joining at an important stage of Reveloop’s data journey. The goal is to move from fragmented data and traditional ETL toward a durable, governed, historical data platform that can power reporting, operational intelligence, automation, AI, and future products.If you enjoy building from the ground up, taking ownership, and solving complex data challenges, we’d love to hear from you.

• Design, build, and manage Reveloop’s data lake and data platform.
• Develop scalable ETL/ELT pipelines using SQL, Python, and modern data engineering tools.
• Build ingestion pipelines for databases, APIs, SaaS applications, files/SFTP, and other data sources.
• Establish Raw/Bronze, Staging/Silver, and Gold/curated data layers.
• Build reliable, testable, and replayable data pipelines with appropriate monitoring and recovery.
• Implement data quality, lineage, governance, security, and access controls.
• Design data models and curated datasets for RCM, payer analytics, claims, denials, finance, operations, and product analytics.
• Design the platform to appropriately protect PHI, PII, financial information, and other sensitive healthcare and business data.
• Establish data contracts and mechanisms for identifying and managing source-system schema changes.
• Help establish the foundation for analytics, automation, AI, and future products.
• Work closely with software engineers, product teams, analysts, finance, operations, and business stakeholders.
• Document architecture, pipelines, schemas, business logic, data lineage, runbooks, dependencies, and operational procedures.
• Design appropriate data partitioning, file organization, storage formats, and lifecycle policies for performance and cost management.
• Establish data lineage from source systems through transformations to final reports, applications, and downstream consumers.

• Strong SQL skills
• Strong Python skills
• Experience designing and operating production ETL/ELT pipelines
• Hands-on experience with a data lake, lakehouse, or modern data platform
• Experience with Azure, preferably Azure Data Lake Storage Gen2
• Experience with data quality, governance, security, monitoring, and lineage
• Experience with Git, code reviews, and CI/CD
• Ability to troubleshoot production data issues
• Translate business requirements into scalable technical solutions
• Strong communication skills
• Ability to work independently

• 5+ years of relevant data engineering or data platform experience, or equivalent demonstrated capability.
• Strong understanding of data modeling, data warehousing, and database architecture.
• Strong understanding of data quality, data governance, security, access controls, and lineage.