Azure Data Engineer: Synapse, Databricks & Python Pipelines

ITMC Systems, Inc; Jackson (MI); On-site; USD 90,000 - 130,000; Full time

Job summary

ITMC Systems, Inc is seeking a data engineer with hands-on Azure experience to design robust data pipelines and analytics solutions. You will work across Azure Synapse, Data Factory, Databricks, and SQL, leveraging Python libraries and Power BI for insights.

The role requires deploying code through DevOps practices and collaborating with teams to ensure reliable data delivery. Strong knowledge of APIs, Spark, and notebooks is required, with a focus on end-to-end data engineering in a production

Qualifications

• Hands-on experience with Azure Synapse Analytics, Data Factory and Databricks.
• Strong data engineering skills across pipelines, SQL, and stored procedures.
• Proficient with Python libraries (PySpark, Pandas, NumPy) and APIs.
• Experience deploying code across dev → QA → Prod with GitHub and CI/CD.
• Knowledge of Spark Pools, Databricks notebooks, and Power BI is essential.

Responsibilities

• Design and implement end-to-end data pipelines and data flows.
• Develop and optimize SQL stored procedures and dynamic SQL logic.
• Integrate data from APIs and troubleshoot data issues across environments.
• Collaborate on DevOps practices for CI/CD and code deployment.
• Work with PySpark, Spark, and Power BI to enable analytics solutions.
• Support users with library installations and debugging data issues.

Skills

Azure Synapse Analytics

Azure Data Factory

Azure Databricks

Azure Storage

SQL

Python (PySpark)

Pandas

NumPy

MySQL

Oracle

Power BI

CI/CD

APIs

GitHub

Spark

Data Pipelines

Tools

Notebooks

Job description

ITMC Systems, Inc is seeking a data engineer with hands-on Azure experience to design robust data pipelines and analytics solutions. You will work across Azure Synapse, Data Factory, Databricks, and SQL, leveraging Python libraries and Power BI for insights.

The role requires deploying code through DevOps practices and collaborating with teams to ensure reliable data delivery. Strong knowledge of APIs, Spark, and notebooks is required, with a focus on end-to-end data engineering in a production