Reveloop

Data Engineer / Data Platform Engineer

Overview

About Reveloop 

Reveloop is building her analytics program with constant change in analytics needs focused on revenue cycle management, automation, software, analytics, and AI. Our reporting platform consumes data from RCM workflows, payer systems, client systems, applications, databases, APIs, financial systems, and internal software products. We are building a modern data platform that turns this information into a reliable, secure, historical source of truth for RCM operations 

The Opportunity 

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 establish the architecture, engineering standards, pipelines, governance, testing, security, monitoring, and operating model for the platform. 

Why This Role Matters at Reveloop 

Reveloop is moving from fragmented operational data and traditional ETL toward a durable, governed, historical data platform. The person in this role will help build that foundation. The objective is not simply to move data from one system to another; it is to create a trusted data layer that Reveloop can use repeatedly for reporting, operational intelligence, automation, AI, product development, and strategic decision-making. 

No. of Vacancies
1
Specific Skills
Required Qualifications 
  • 5+ years of relevant data engineering, data platform, or equivalent experience, or demonstrated ability to perform the responsibilities at this level. 
  • Strong SQL skills and strong Python programming ability. 
  • Demonstrated experience designing and operating production ETL/ELT pipelines. 
  • Hands-on experience designing or managing a data lake, lakehouse, or modern data platform. 
  • Strong understanding of data modeling, data warehousing, dimensional modeling, and database architecture. 
  • Experience with Azure and preferably Azure Data Lake Storage Gen2. 
  • Experience with modern transformation and testing practices, preferably dbt. 
  • Experience with workflow orchestration and production pipeline monitoring. 
  • Experience working with relational databases, APIs, files/SFTP, and third-party systems. 
  • Strong understanding of data quality, data governance, security, access controls, and lineage. 
  • Experience using Git and software engineering practices such as code review and CI/CD. 
  • Ability to troubleshoot production data issues and identify root causes. 
  • Ability to translate business requirements into scalable technical data solutions. 
  • Strong communication skills and ability to work independently across technical and business teams. 
Responsible For
What You Will Own 
  • Build and manage Reveloop’s Azure Data Lake Storage Gen2 environment as the centralized historical data layer. 
  • Design scalable ingestion patterns for databases, APIs, SaaS applications, flat files, SFTP feeds, application databases, and other internal/external systems. 
  • Preserve raw source data in the Raw/Bronze layer so historical data can be reconstructed, audited, recovered, and reprocessed. 
  • Build staging, intermediate, business-logic, and Gold/mart layers that transform raw data into trusted, consumable datasets. 
  • Develop and maintain production-grade ETL/ELT pipelines using SQL, Python, and appropriate data engineering technologies. 
  • Implement incremental processing, change-data-capture where appropriate, deduplication, reconciliation, backfills, and restartable/recoverable workflows. 
  • Design pipelines to be idempotent and replayable so historical raw data can be reprocessed when source data or business logic changes. 
  • Use dbt or comparable transformation tooling to establish controlled, versioned, testable transformations and publishing workflows. 
  • Build automated data-quality tests for completeness, uniqueness, validity, referential integrity, freshness, consistency, and business rules. 
  • Establish data contracts and mechanisms for identifying and managing source-system schema changes. 
  • Design appropriate data partitioning, file organization, storage formats, and lifecycle policies for performance and cost management. 
  • Build curated datasets and data marts supporting RCM, payer analytics, authorization, VOB, claims, denials, collections, payment posting, financial reporting, operations, and product analytics. 
  • Create reliable data foundations for AI, machine learning, automation, and agentic workflows. 
  • Implement orchestration, scheduling, dependency management, retries, failure handling, alerting, and SLA monitoring. 
  • Monitor production pipelines and investigate data freshness, volume, latency, quality, and infrastructure issues. 
  • Build observability and operational dashboards that allow the team to understand the health of the data platform. 
  • Establish disaster recovery and reprocessing capabilities so critical data and transformations can be restored without unnecessarily depending on source systems. 
  • Implement data retention, archival, backup, and recovery strategies appropriate to each class of data. 
  • Establish data lineage from source systems through transformations to final reports, applications, and downstream consumers. 
  • Implement data cataloging and metadata standards so users can understand what datasets exist, what they mean, where they came from, and how they are used. 
  • Implement role-based access, authentication, authorization, encryption, secrets management, audit logging, and least-privilege access. 
  • Design the platform to appropriately protect PHI, PII, financial information, and other sensitive healthcare and business data. 
  • Optimize cloud storage, compute, queries, pipeline performance, and overall data-platform cost. 
  • Create development, testing, staging, and production practices for data engineering code and infrastructure. 
  • Use Git, code review, CI/CD, and automated deployment practices to improve reliability and maintainability. 
  • Document architecture, pipelines, schemas, business logic, data lineage, runbooks, dependencies, and operational procedures. 
  • Partner with software engineers, product teams, RCM leaders, analysts, finance, operations, and executive stakeholders to translate business requirements into durable data solutions. 
Additional Requirements
Preferred Qualifications 
  • Experience with Azure Databricks, Microsoft Fabric, Synapse, Snowflake, BigQuery, Redshift, or comparable platforms. 
  • Experience with Delta Lake and/or Delta tables. 
  • Experience with Airflow, Dagster, Prefect, Azure Data Factory, or comparable orchestration tools. 
  • Experience with dbt. 
  • Experience with CDC technologies and streaming/event-driven architectures. 
  • Experience with Terraform or other infrastructure-as-code tools. 
  • Experience with Docker and modern CI/CD workflows. 
  • Experience with data catalog, metadata, lineage, and observability tools. 
  • Experience designing healthcare, RCM, claims, payer, financial, or other highly sensitive data platforms. 
  • Experience supporting analytics, machine learning, AI, automation, or agentic workflows. 
Job Nature
Full Time
Educational Requirements
Education  Bachelor’s degree in Computer Science, Information Technology, Software Engineering, Data Engineering, or a related field preferred. Equivalent professional experience and demonstrated technical capability may be considered. 
Experience Requirements
5+ Years
Job Level
Sr. Position

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