Remote Data Engineer: API + Fabric Pipelines

Optomi; Detroit (MI); Remote; USD 80,000 - 110,000; Full time

Benefits offered by this job

100% remote

Job summary

Optomi is seeking a Data Analyst to join a growing data team in a fully remote capacity. The role focuses on ingesting and shaping data from REST APIs and third-party sources into Microsoft Fabric and Azure-based environments.

You will work hands-on with Fabric Lakehouse, OneLake, and pipelines, collaborating with data engineers and stakeholders while growing toward Data Engineering and Data Architecture roles.

Qualifications

• 3+ years of hands-on data analytics, data engineering, or data integration experience.
• Hands-on experience with Microsoft Fabric and Azure data services.
• Strong Python and SQL for data ingestion, transformation, and validation.
• Experience ingesting data via REST APIs and external sources.
• Experience with ETL/ELT pipelines and data quality monitoring.

Responsibilities

• Design, build, and maintain ETL/ELT pipelines ingesting data from third-party vendors.
• Develop REST API integrations to bring external data into Fabric/Azure.
• Use Python and SQL to clean, transform, and standardize data.
• Store ingested data using Fabric, OneLake, Lakehouse, and Azure services.
• Create clean datasets for downstream analytics and engineering teams.
• Monitor pipelines, troubleshoot failures, and resolve data quality issues.
• Document sources, schemas, and transformation logic; coordinate with stakeholders.

Skills

Microsoft Fabric

Python

SQL

REST API ingestion

ETL/ELT pipelines

Data integration

Azure

Data modeling

Tools

Fabric Lakehouse

OneLake

Fabric Pipelines

Azure Data Factory

Azure SQL

Azure Data Lake Storage

Job description

Optomi is seeking a Data Analyst to join a growing data team in a fully remote capacity. The role focuses on ingesting and shaping data from REST APIs and third-party sources into Microsoft Fabric and Azure-based environments.

You will work hands-on with Fabric Lakehouse, OneLake, and pipelines, collaborating with data engineers and stakeholders while growing toward Data Engineering and Data Architecture roles.