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# Senior Data Ops Engineer

- **Company:** Fractal Analytics
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $130,000.0 - $160,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Microsoft Azure, BigQuery, Continuous Integration, Data Infrastructure, Extract Transform Load (ETL), Github, Standard Sql, DataOps, SQL Databases, Fast Healthcare Interoperability Resources, Snowflake, Multi-Cloud, Git, Pyspark, Information Technology, Health Level Seven International, Machine Learning Operations, Data Delivery, Data Pipelines, Databricks
- **Published:** July 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=092a3008f1f3de0c

## About the Role

* Azure Databricks (Mandatory) - PySpark, SQL
 * Azure Services - ADF, ADLS, Synapse
 * Data Orchestration - Airflow / ADF / Databricks Workflows
 * CI/CD Tools - Azure DevOps / GitHub / Git
 * Strong expertise in ETL/ELT pipelines, DataOps, and production support
 * Experience with data quality, monitoring, and observability frameworks

Preferred Skills

 * Experience with Snowflake / BigQuery / multi-cloud environments
 * Exposure to MLOps / AI data pipelines
 * Knowledge of Medallion Architecture (Bronze-Silver-Gold)

Domain Experience (Good to Have)

 * US Healthcare domain knowledge:
 + Claims, Member, Provider, Eligibility data
 + Familiarity with FHIR / HL7 / EMR data models

Experience: 10+ Years

Education: Bachelor's Degree (4-year) - Computer Science / Engineering / Related Field

## Description

We are hiring a Senior DataOps Engineer to drive enterprise DataOps capabilities, ensuring reliable, scalable, and production-ready data pipelines on Azure Databricks platforms. This role focuses on pipeline orchestration, CI/CD automation, monitoring, and data platform operations to support analytics and AI/ML workloads., * Build and manage end-to-end data pipelines using Azure Databricks (PySpark, SQL)

 * Implement workflow orchestration (Airflow / ADF / Databricks Workflows)
 * Establish Data Ops practices: monitoring, alerting, and production support Drive CI/CD pipelines and environment promotion (Dev
 * Prod)
 * Ensure data quality, lineage, and governance compliance
 * Manage incident resolution, RCA, and SLA adherence
 * Optimize pipeline performance, scalability, and cost
 * Data Ops function ensures consistent, reliable data delivery and operational stability across platforms


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