Data Analyst

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, in partnership with a leading organization in the restaurant industry, is looking for a Data Analyst to join their growing data team! This is an exciting opportunity for a hands-on data professional to help build and maintain data integrations that bring third-party and vendor data into a modern Microsoft Fabric and Azure environment. This role will focus heavily on Microsoft Fabric, Python, SQL, and REST API ingestion while providing long-term growth opportunities into Data Engineering and Data Architecture.

What the right candidate will enjoy:

• 100% remote opportunity!
• Working hands-on with Microsoft Fabric, Azure, Python, and SQL in a modern data environment!
• Building data ingestion processes that bring data from REST APIs and third-party sources into Microsoft Fabric and Azure!
• Working with Fabric Lakehouse, OneLake, pipelines, and other modern Microsoft data technologies to create clean, reliable datasets!
• Partnering closely with data engineers and technical stakeholders while having the opportunity to grow further into Data Engineering and Data Architecture!

Experience the right candidate will have:

• 3+ years of hands-on Data Analytics, Data Engineering, or Data Integration experience.
• Strong hands-on experience with Microsoft Fabric, including technologies such as OneLake, Lakehouse, Fabric Pipelines, Dataflows Gen2, or Fabric Notebooks.
• Strong Python and SQL experience for data ingestion, transformation, cleaning, and validation.
• Experience consuming and ingesting data from REST APIs and other external or third-party data sources.
• Hands-on experience within the Microsoft Azure ecosystem, ideally including Azure Data Factory, Azure SQL, or Azure Data Lake Storage.
• Experience designing, building, and maintaining ETL/ELT pipelines.
• Experience working with structured and semi-structured data, including formats such as JSON and CSV.
• Strong understanding of data quality, validation, troubleshooting, and monitoring.
• Experience with data modeling, schema design, Git/version control, or data governance is a plus.
• Experience working with vendor data feeds and coordinating data integration requirements is a plus.

Responsibilities of the right candidate:

• Design, build, and maintain ETL/ELT pipelines that ingest data from third-party vendors and external sources.
• Develop REST API integrations to bring external data into Microsoft Fabric and Azure environments.
• Use Python and SQL to clean, validate, transform, and standardize incoming data.
• Store and organize ingested data using Microsoft Fabric, OneLake, Lakehouse, and Azure data services.
• Create clean, structured, analysis-ready datasets that can be consumed by downstream data engineering and analytics teams.
• Monitor pipeline health, troubleshoot failures, and resolve data quality issues.
• Document data sources, schemas, transformation logic, refresh schedules, dependencies, and known data issues.
• Partner with data engineers and internal stakeholders to understand downstream requirements and ensure datasets meet business and technical needs.
• Coordinate with third-party vendors around data delivery, format changes, access, and integration requirements.